Discovering complex matchings across web query interfaces : a correlation mining approach To enable information integration , schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources . While complex matchings are common , because of their far more complex search space , most existing techniques focus on simple 1:1 matchings . To tackle this challenge , this paper takes a conceptually novel approach by viewing schema matching as correlation mining , for our task of matching Web query interfaces to integrate the myriad databases on the Internet . On this `` deep Web , '' query interfaces generally form complex matchings between attribute groups ( e.g. , ( author ) corresponds to ( first name , last name ) in the Books domain ) . We observe that the co-occurrences patterns across query interfaces often reveal such complex semantic relationships : grouping attributes ( e.g. , ( first name , last name ) ) tend to be co-present in query interfaces and thus positively correlated . In contrast , synonym attributes are negatively correlated because they rarely co-occur . This insight enables us to discover complex matchings by a correlation mining approach . In particular , we develop the DCM framework , which consists of data preparation , dual mining of positive and negative correlations , and finally matching selection . Unlike previous correlation mining algorithms , which mainly focus on finding strong positive correlations , our algorithm cares both positive and negative correlations , especially the subtlety of negative correlations , due to its special importance in schema matching . This leads to the introduction of a new correlation measure , $ H$ - measure , distinct from those proposed in previous work . We evaluate our approach extensively and the results show good accuracy for discovering complex matchings .

O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

correlation measure
correlation mining
deep web
schema matching

data integration
heterogeneous databases


Stable feature selection via dense feature groups Many feature selection algorithms have been proposed in the past focusing on improving classification accuracy . In this work , we point out the importance of stable feature selection for knowledge discovery from high-dimensional data , and identify two causes of instability of feature selection algorithms : selection of a minimum subset without redundant features and small sample size . We propose a general framework for stable feature selection which emphasizes both good generalization and stability of feature selection results . The framework identifies dense feature groups based on kernel density estimation and treats features in each dense group as a coherent entity for feature selection . An efficient algorithm DRAGS ( Dense Relevant Attribute Group Selector ) is developed under this framework . We also introduce a general measure for assessing the stability of feature selection algorithms . Our empirical study based on microarray data verifies that dense feature groups remain stable under random sample hold out , and the DRAGS algorithm is effective in identifying a set of feature groups which exhibit both high classification accuracy and stability .

O B I O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O B I O O O O O O O O O O B I I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O

classification
feature selection
kernel density estimation
stability


A system for real-time competitive market intelligence A method is described for real-time market intelligence and competitive analysis . News stories are collected online for a designated group of companies . The goal is to detect critical differences in the text written about a company versus the text for its competitors . A solution is found by mapping the task into a non-stationary text categorization model . The overall design consists of the following components : ( a ) a real-time crawler that monitors newswires for stories about the competitors ( b ) a conditional document retriever that selects only those documents that meet the indicated conditions ( c ) text analysis techniques that convert the documents to a numerical format ( d ) rule induction methods for finding patterns in data ( e ) presentation techniques for displaying results . The method is extended to combine text with numerical measures , such as those based on stock prices and market capitalizations , that allow for more objective evaluations and projections .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Ensemble pruning via individual contribution ordering An ensemble is a set of learned models that make decisions collectively . Although an ensemble is usually more accurate than a single learner , existing ensemble methods often tend to construct unnecessarily large ensembles , which increases the memory consumption and computational cost . Ensemble pruning tackles this problem by selecting a subset of ensemble members to form subensembles that are subject to less resource consumption and response time with accuracy that is similar to or better than the original ensemble . In this paper , we analyze the accuracy\/diversity trade-off and prove that classifiers that are more accurate and make more predictions in the minority group are more important for subensemble construction . Based on the gained insights , a heuristic metric that considers both accuracy and diversity is proposed to explicitly evaluate each individual classifier 's contribution to the whole ensemble . By incorporating ensemble members in decreasing order of their contributions , subensembles are formed such that users can select the top $ p $ percent of ensemble members , depending on their resource availability and tolerable waiting time , for predictions . Experimental results on 26 UCI data sets show that subensembles formed by the proposed EPIC ( Ensemble Pruning via Individual Contribution ordering ) algorithm outperform the original ensemble and a state-of-the-art ensemble pruning method , Orientation Ordering ( OO ) .

B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O


Generalized component analysis for text with heterogeneous attributes We present a class of richly structured , undirected hidden variable models suitable for simultaneously modeling text along with other attributes encoded in different modalities . Our model generalizes techniques such as principal component analysis to heterogeneous data types . In contrast to other approaches , this framework allows modalities such as words , authors and timestamps to be captured in their natural , probabilistic encodings . A latent space representation for a previously unseen document can be obtained through a fast matrix multiplication using our method . We demonstrate the effectiveness of our framework on the task of author prediction from 13 years of the NIPS conference proceedings and for a recipient prediction task using a 10-month academic email archive of a researcher . Our approach should be more broadly applicable to many real-world applications where one wishes to efficiently make predictions for a large number of potential outputs using dimensionality reduction in a well defined probabilistic framework .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

learning
multimodal heterogeneous data
text mining
topic modeling
undirected graphical models


B-EM : a classifier incorporating bootstrap with EM approach for data mining This paper investigates the problem of augmenting labeled data with unlabeled data to improve classification accuracy . This is significant for many applications such as image classification where obtaining classification labels is expensive , while large unlabeled examples are easily available . We investigate an Expectation Maximization ( EM ) algorithm for learning from labeled and unlabeled data . The reason why unlabeled data boosts learning accuracy is because it provides the information about the joint probability distribution . A theoretical argument shows that the more unlabeled examples are combined in learning , the more accurate the result . We then introduce B-EM algorithm , based on the combination of EM with bootstrap method , to exploit the large unlabeled data while avoiding prohibitive I\/O cost . Experimental results over both synthetic and real data sets that the proposed approach has a satisfactory performance .

O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

bootstrap method
classification
expectation maximization

miscellaneous
supervised and unsupervised learning


Mining for misconfigured machines in grid systems Grid systems are proving increasingly useful for managing the batch computing jobs of organizations . One well-known example is Intel , whose internally developed NetBatch system manages tens of thousands of machines . The size , heterogeneity , and complexity of grid systems make them very difficult , however , to configure . This often results in misconfigured machines , which may adversely affect the entire system . We investigate a distributed data mining approach for detection of misconfigured machines . Our Grid Monitoring System ( GMS ) non-intrusively collects data from all sources ( log files , system services , etc. ) available throughout the grid system . It converts raw data to semantically meaningful data and stores this data on the machine it was obtained from , limiting incurred overhead and allowing scalability . Afterwards , when analysis is requested , a distributed outliers detection algorithm is employed to identify misconfigured machines . The algorithm itself is implemented as a recursive workflow of grid jobs . It is especially suited to grid systems , in which the machines might be unavailable most of the time and often fail altogether .

O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O

distributed data mining
grid systems
outliers detection

distributed systems
grid information system
system monitoring


Multiple domain user personalization Content personalization is a key tool in creating attractive websites . Synergies can be obtained by integrating personalization between several Internet properties . In this paper we propose a hierarchical Bayesian model to address these issues . Our model allows the integration of multiple properties without changing the overall structure , which makes it easily extensible across large Internet portals . It relies at its lowest level on Latent Dirichlet Allocation , while making use of latent side features for cross-property integration . We demonstrate the efficiency of our approach by analyzing data from several properties of a major Internet portal .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

domain integration
learning
parallel statistical inference
user profiling


Finding recent frequent itemsets adaptively over online data streams A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate . Consequently , the knowledge embedded in a data stream is more likely to be changed as time goes by . Identifying the recent change of a data stream , specially for an online data stream , can provide valuable information for the analysis of the data stream . In addition , monitoring the continuous variation of a data stream enables to find the gradual change of embedded knowledge . However , most of mining algorithms over a data stream do not differentiate the information of recently generated transactions from the obsolete information of old transactions which may be no longer useful or possibly invalid at present . This paper proposes a data mining method for finding recent frequent itemsets adaptively over an online data stream . The effect of old transactions on the mining result of the data steam is diminished by decaying the old occurrences of each itemset as time goes by . Furthermore , several optimization techniques are devised to minimize processing time as well as main memory usage . Finally , the proposed method is analyzed by a series of experiments .

O B I I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

database applications
decay mechanism
delayed-insertion
pruning of itemsets


On string classification in data streams String data has recently become important because of its use in a number of applications such as computational and molecular biology , protein analysis , and market basket data . In many cases , these strings contain a wide variety of substructures which may have physical significance for that application . For example , such substructures could represent important fragments of a DNA string or an interesting portion of a fraudulent transaction . In such a case , it is desirable to determine the identity , location , and extent of that substructure in the data . This is a much more difficult generalization of the classification problem , since the latter problem labels entire strings rather than deal with the more complex task of determining string fragments with a particular kind of behavior . The problem becomes even more complicated when different kinds of substrings show complicated nesting patterns . Therefore , we define a somewhat different problem which we refer to as the generalized classification problem . We propose a scalable approach based on hidden markov models for this problem . We show how to implement the generalized string classification procedure for very large data bases and data streams . We present experimental results over a number of large data sets and data streams .

O B B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I I O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O

classification
hidden markov models
string


Efficient discovery of error-tolerant frequent itemsets in high dimensions We present a generalization of frequent itemsets allowing for the notion of errors in the itemset definition . We motivate the problem and present an efficient algorithm that identifies error-tolerant frequent clusters of items in transactional data ( customer-purchase data , web browsing data , text , etc. ) . The algorithm exploits sparseness of the underlying data to find large groups of items that are correlated over database records ( rows ) . The notion of transaction coverage allows us to extend the algorithm and view it as a fast clustering algorithm for discovering segments of similar transactions in binary sparse data . We evaluate the new algorithm on three real-world applications : clustering high-dimensional data , query selectivity estimation and collaborative filtering . Results show that the algorithm consistently uncovers structure in large sparse databases that other traditional clustering algorithms fail to find .

O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O B I O O O O O O O O O O O O O O O O O O O O O O

collaborative filtering
high dimensions
query selectivity estimation


Constrained optimization for validation-guided conditional random field learning Conditional random fields ( CRFs ) are a class of undirected graphical models which have been widely used for classifying and labeling sequence data . The training of CRFs is typically formulated as an unconstrained optimization problem that maximizes the conditional likelihood . However , maximum likelihood training is prone to overfitting . To address this issue , we propose a novel constrained nonlinear optimization formulation in which the prediction accuracy of cross-validation sets are included as constraints . Instead of requiring multiple passes of training , the constrained formulation allows the cross-validation be handled in one pass of constrained optimization . The new formulation is discontinuous , and classical Lagrangian based constraint handling methods are not applicable . A new constrained optimization algorithm based on the recently proposed extended saddle point theory is developed to learn the constrained CRF model . Experimental results on gene and stock-price prediction tasks show that the constrained formulation is able to significantly improve the generalization ability of CRF training .

B I O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

conditional random fields
constrained optimization

cross validation
extended saddle points
financial


The cost of privacy : destruction of data-mining utility in anonymized data publishing Re-identification is a major privacy threat to public datasets containing individual records . Many privacy protection algorithms rely on generalization and suppression of `` quasi-identifier '' attributes such as ZIP code and birthdate . Their objective is usually syntactic sanitization : for example , k-anonymity requires that each `` quasi-identifier '' tuple appear in at least k records , while l-diversity requires that the distribution of sensitive attributes for each quasi-identifier have high entropy . The utility of sanitized data is also measured syntactically , by the number of generalization steps applied or the number of records with the same quasi-identifier . In this paper , we ask whether generalization and suppression of quasi-identifiers offer any benefits over trivial sanitization which simply separates quasi-identifiers from sensitive attributes . Previous work showed that k-anonymous databases can be useful for data mining , but k-anonymization does not guarantee any privacy . By contrast , we measure the tradeoff between privacy ( how much can the adversary learn from the sanitized records ? ) and utility , measured as accuracy of data-mining algorithms executed on the same sanitized records . For our experimental evaluation , we use the same datasets from the UCI machine learning repository as were used in previous research on generalization and suppression . Our results demonstrate that even modest privacy gains require almost complete destruction of the data-mining utility . In most cases , trivial sanitization provides equivalent utility and better privacy than k-anonymity , l-diversity , and similar methods based on generalization and suppression .

O O O B O O O O B O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O B O O B O O O O O O O O O O O O O O


Robust boosting and its relation to bagging Several authors have suggested viewing boosting as a gradient descent search for a good fit in function space . At each iteration observations are re-weighted using the gradient of the underlying loss function . We present an approach of weight decay for observation weights which is equivalent to `` robustifying '' the underlying loss function . At the extreme end of decay this approach converges to Bagging , which can be viewed as boosting with a linear underlying loss function . We illustrate the practical usefulness of weight decay for improving prediction performance and present an equivalence between one form of weight decay and `` Huberizing '' -- a statistical method for making loss functions more robust .

O B O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Indexing multi-dimensional time-series with support for multiple distance measures Although most time-series data mining research has concentrated on providing solutions for a single distance function , in this work we motivate the need for a single index structure that can support multiple distance measures . Our specific area of interest is the efficient retrieval and analysis of trajectory similarities . Trajectory datasets are very common in environmental applications , mobility experiments , video surveillance and are especially important for the discovery of certain biological patterns . Our primary similarity measure is based on the Longest Common Subsequence ( LCSS ) model , that offers enhanced robustness , particularly for noisy data , which are encountered very often in real world applications . However , our index is able to accommodate other distance measures as well , including the ubiquitous Euclidean distance , and the increasingly popular Dynamic Time Warping ( DTW ) . While other researchers have advocated one or other of these similarity measures , a major contribution of our work is the ability to support all these measures without the need to restructure the index . Our framework guarantees no false dismissals and can also be tailored to provide much faster response time at the expense of slightly reduced precision\/recall . The experimental results demonstrate that our index can help speed-up the computation of expensive similarity measures such as the LCSS and the DTW .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

dynamic time warping
longest common subsequence


Data filtering for automatic classification of rocks from reflectance spectra The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites . For instance , NASA intends to design robots that are sufficiently autonomous to perform this task on planetary missions . Spectrometer readings provide one important source of data for identifying sites with minerals of interest . Reflectance spectrometers measure intensities of light reflected from surfaces over a range of wavelengths . Spectral intensity patterns may in some cases be sufficiently distinctive for proper identification of minerals or classes of minerals . For some mineral classes , carbonates for example , specific short spectral intervals are known to carry a distinctive signature . Finding similar distinctive spectral ranges for other mineral classes is not an easy problem . We propose and evaluate data-driven techniques that automatically search for spectral ranges optimized for specific minerals . In one set of studies , we partition the whole interval of wavelengths available in our data into sub-intervals , or bins , and use a genetic algorithm to evaluate a candidate selection of subintervals . As alternatives to this computationally expensive search technique , we present an entropy-based heuristic that gives higher scores for wavelengths more likely to distinguish between classes , as well as other greedy search procedures . Results are presented for four different classes , showing reasonable improvements in identifying some , but not all , of the mineral classes tested .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Beyond classification and ranking : constrained optimization of the ROI Classification has been commonly used in many data mining projects in the financial service industry . For instance , to predict collectability of accounts receivable , a binary class label is created based on whether a payment is received within a certain period . However , optimization of the classifier does not necessarily lead to maximization of return on investment ( ROI ) , since maximization of the true positive rate is often different from maximization of the collectable amount which determines the ROI under a fixed budget constraint . The typical cost sensitive learning does not solve this problem either since it involves an unknown opportunity cost due to the budget constraint . Learning the ranks of collectable amount would ultimately solve the problem , but it tries to tackle an unnecessarily difficult problem and often results in poorer results for our specific target . We propose a new algorithm that uses gradient descent to directly optimize the related monetary measure under the budget constraint and thus maximizes the ROI . By comparison with several classification , regression , and ranking algorithms , we demonstrate the new algorithm 's substantial improvement of the financial impact on our clients in the financial service industry .

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

constrained optimization
return on investment


BoostCluster : boosting clustering by pairwise constraints Data clustering is an important task in many disciplines . A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints . However , these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints . We present a boosting framework for data clustering , termed as BoostCluster , that is able to iteratively improve the accuracy of any given clustering algorithm by exploiting the pairwise constraints . The key challenge in designing a boosting framework for data clustering is how to influence an arbitrary clustering algorithm with the side information since clustering algorithms by definition are unsupervised . The proposed framework addresses this problem by dynamically generating new data representations at each iteration that are , on the one hand , adapted to the clustering results at previous iterations by the given algorithm , and on the other hand consistent with the given side information . Our empirical study shows that the proposed boosting framework is effective in improving the performance of a number of popular clustering algorithms ( K-means , partitional SingleLink , spectral clustering ) , and its performance is comparable to the state-of-the-art algorithms for data clustering with side information .

O O B O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O

boosting
data clustering
pairwise constraints


A scalable two-stage approach for a class of dimensionality reduction techniques Dimensionality reduction plays an important role in many data mining applications involving high-dimensional data . Many existing dimensionality reduction techniques can be formulated as a generalized eigenvalue problem , which does not scale to large-size problems . Prior work transforms the generalized eigenvalue problem into an equivalent least squares formulation , which can then be solved efficiently . However , the equivalence relationship only holds under certain assumptions without regularization , which severely limits their applicability in practice . In this paper , an efficient two-stage approach is proposed to solve a class of dimensionality reduction techniques , including Canonical Correlation Analysis , Orthonormal Partial Least Squares , linear Discriminant Analysis , and Hypergraph Spectral Learning . The proposed two-stage approach scales linearly in terms of both the sample size and data dimensionality . The main contributions of this paper include ( 1 ) we rigorously establish the equivalence relationship between the proposed two-stage approach and the original formulation without any assumption ; and ( 2 ) we show that the equivalence relationship still holds in the regularization setting . We have conducted extensive experiments using both synthetic and real-world data sets . Our experimental results confirm the equivalence relationship established in this paper . Results also demonstrate the scalability of the proposed two-stage approach .

O O O O O O O O B I O B I O O O O O O O O O O O O O O O B I O O O O O O B I I O O O O O O O O O O O O O B I I O O O B I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O

dimensionality reduction
generalized eigenvalue problem
least squares
regularization
scalability


Efficient data reduction with EASE A variety of mining and analysis problems -- ranging from association-rule discovery to contingency table analysis to materialization of certain approximate datacubes -- involve the extraction of knowledge from a set of categorical count data . Such data can be viewed as a collection of `` transactions , '' where a transaction is a fixed-length vector of counts . Classical algorithms for solving count-data problems require one or more computationally intensive passes over the entire database and can be prohibitively slow . One effective method for dealing with this ever-worsening scalability problem is to run the algorithms on a small sample of the data . We present a new data-reduction algorithm , called EASE , for producing such a sample . Like the FAST algorithm introduced by Chen et al. , EASE is especially designed for count data applications . Both EASE and FAST take a relatively large initial random sample and then deterministically produce a subsample whose `` distance '' -- appropriately defined -- from the complete database is minimal . Unlike FAST , which obtains the final subsample by quasi-greedy descent , EASE uses epsilon-approximation methods to obtain the final subsample by a process of repeated halving . Experiments both in the context of association rule mining and classical χ2 contingency-table analysis show that EASE outperforms both FAST and simple random sampling , sometimes dramatically .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O

association rules
count dataset
data streams
database applications
frequency estimation
olap


The predictive power of online chatter An increasing fraction of the global discourse is migrating online in the form of blogs , bulletin boards , web pages , wikis , editorials , and a dizzying array of new collaborative technologies . The migration has now proceeded to the point that topics reflecting certain individual products are sufficiently popular to allow targeted online tracking of the ebb and flow of chatter around these topics . Based on an analysis of around half a million sales rank values for 2,340 books over a period of four months , and correlating postings in blogs , media , and web pages , we are able to draw several interesting conclusions . First , carefully hand-crafted queries produce matching postings whose volume predicts sales ranks . Second , these queries can be automatically generated in many cases . And third , even though sales rank motion might be difficult to predict in general , algorithmic predictors can use online postings to successfully predict spikes in sales rank .

O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O

information search and retrieval
prediction
time-series analysis


Large-scale behavioral targeting Behavioral targeting ( BT ) leverages historical user behavior to select the ads most relevant to users to display . The state-of-the-art of BT derives a linear Poisson regression model from fine-grained user behavioral data and predicts click-through rate ( CTR ) from user history . We designed and implemented a highly scalable and efficient solution to BT using Hadoop MapReduce framework . With our parallel algorithm and the resulting system , we can build above 450 BT-category models from the entire Yahoo 's user base within one day , the scale that one can not even imagine with prior systems . Moreover , our approach has yielded 20 % CTR lift over the existing production system by leveraging the well-grounded probabilistic model fitted from a much larger training dataset . Specifically , our major contributions include : ( 1 ) A MapReduce statistical learning algorithm and implementation that achieve optimal data parallelism , task parallelism , and load balance in spite of the typically skewed distribution of domain data . ( 2 ) An in-place feature vector generation algorithm with linear time complexity O ( n ) regardless of the granularity of sliding target window . ( 3 ) An in-memory caching scheme that significantly reduces the number of disk IOs to make large-scale learning practical . ( 4 ) Highly efficient data structures and sparse representations of models and data to enable fast model updates . We believe that our work makes significant contributions to solving large-scale machine learning problems of industrial relevance in general . Finally , we report comprehensive experimental results , using industrial proprietary codebase and datasets .

O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

design methodology
grid computing
large-scale


Finding tribes : identifying close-knit individuals from employment patterns We present a family of algorithms to uncover tribes-groups of individuals who share unusual sequences of affiliations . While much work inferring community structure describes large-scale trends , we instead search for small groups of tightly linked individuals who behave anomalously with respect to those trends . We apply the algorithms to a large temporal and relational data set consisting of millions of employment records from the National Association of Securities Dealers . The resulting tribes contain individuals at higher risk for fraud , are homogenous with respect to risk scores , and are geographically mobile , all at significant levels compared to random or to other sets of individuals who share affiliations .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

anomaly detection
dynamic networks
social networks


Reducing the human overhead in text categorization Many applications in text processing require significant human effort for either labeling large document collections ( when learning statistical models ) or extrapolating rules from them ( when using knowledge engineering ) . In this work , we describe away to reduce this effort , while retaining the methods ' accuracy , by constructing a hybrid classifier that utilizes human reasoning over automatically discovered text patterns to complement machine learning . Using a standard sentiment-classification dataset and real customer feedback data , we demonstrate that the resulting technique results in significant reduction of the human effort required to obtain a given classification accuracy . Moreover , the hybrid text classifier also results in a significant boost in accuracy over machine-learning based classifiers when a comparable amount of labeled data is used .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

active learning
supervised learning
support vector machines
text classification
text mining


SEWeP : using site semantics and a taxonomy to enhance the Web personalization process Web personalization is the process of customizing a Web site to the needs of each specific user or set of users , taking advantage of the knowledge acquired through the analysis of the user 's navigational behavior . Integrating usage data with content , structure or user profile data enhances the results of the personalization process . In this paper , we present SEWeP , a system that makes use of both the usage logs and the semantics of a Web site 's content in order to personalize it . Web content is semantically annotated using a conceptual hierarchy ( taxonomy ) . We introduce C-logs , an extended form of Web usage logs that encapsulates knowledge derived from the link semantics . C-logs are used as input to the Web usage mining process , resulting in a broader yet semantically focused set of recommendations .

O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

concept hierarchies
database applications
on-line information services
semantic annotation of web content
web mining


From frequent itemsets to semantically meaningful visual patterns Data mining techniques that are successful in transaction and text data may not be simply applied to image data that contain high-dimensional features and have spatial structures . It is not a trivial task to discover meaningful visual patterns in image databases , because the content variations and spatial dependency in the visual data greatly challenge most existing methods . This paper presents a novel approach to coping with these difficulties for mining meaningful visual patterns . Specifically , the novelty of this work lies in the following new contributions : ( 1 ) a principled solution to the discovery of meaningful itemsets based on frequent itemset mining ; ( 2 ) a self-supervised clustering scheme of the high-dimensional visual features by feeding back discovered patterns to tune the similarity measure through metric learning ; and ( 3 ) a pattern summarization method that deals with the measurement noises brought by the image data . The experimental results in the real images show that our method can discover semantically meaningful patterns efficiently and effectively .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

image data mining
meaningful itemset mining
self-supervised clustering


Practical learning from one-sided feedback In many data mining applications , online labeling feedback is only available for examples which were predicted to belong to the positive class . Such applications includespam filtering in the case where users never checkemails marked `` spam '' , document retrieval where users cannotgive relevance feedback on unretrieved documents , and online advertising where user behavior can not beobserved for unshown advertisements . One-sided feedback can cripple the performance of classical mistake-driven online learners such as Perceptron . Previous work under the Apple Tasting framework showed how to transform standard online learners into successful learners from one sided feedback . However , we find in practice that this transformation may request more labels than necessary to achieve strong performance . In this paper , we employ two active learning methods which reduce the number of labels requested in practice . One method is the use of Label Efficient active learning . The other method , somewhat surprisingly , is the use of margin-based learners without modification , which we show combines implicit active learning and a greedy strategy to managing the exploration exploitation tradeoff . Experimental results show that these methods can be significantly more effective in practice than those using the Apple Tasting transformation , even on minority class problems .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O


A probabilistic framework for relational clustering Relational clustering has attracted more and more attention due to its phenomenal impact in various important applications which involve multi-type interrelated data objects , such as Web mining , search marketing , bioinformatics , citation analysis , and epidemiology . In this paper , we propose a probabilistic model for relational clustering , which also provides a principal framework to unify various important clustering tasks including traditional attributes-based clustering , semi-supervised clustering , co-clustering and graph clustering . The proposed model seeks to identify cluster structures for each type of data objects and interaction patterns between different types of objects . Under this model , we propose parametric hard and soft relational clustering algorithms under a large number of exponential family distributions . The algorithms are applicable to relational data of various structures and at the same time unifies a number of stat-of-the-art clustering algorithms : co-clustering algorithms , the k-partite graph clustering , Bregman k-means , and semi-supervised clustering based on hidden Markov random fields .

O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Random projection in dimensionality reduction : applications to image and text data Random projections have recently emerged as a powerful method for dimensionality reduction . Theoretical results indicate that the method preserves distances quite nicely ; however , empirical results are sparse . We present experimental results on using random projection as a dimensionality reduction tool in a number of cases , where the high dimensionality of the data would otherwise lead to burden-some computations . Our application areas are the processing of both noisy and noiseless images , and information retrieval in text documents . We show that projecting the data onto a random lower-dimensional subspace yields results comparable to conventional dimensionality reduction methods such as principal component analysis : the similarity of data vectors is preserved well under random projection . However , using random projections is computationally significantly less expensive than using , e.g. , principal component analysis . We also show experimentally that using a sparse random matrix gives additional computational savings in random projection .

B I O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O

dimensionality reduction
random projection

high-dimensional data
image data
text document data


Constant-factor approximation algorithms for identifying dynamic communities We propose two approximation algorithms for identifying communities in dynamic social networks . Communities are intuitively characterized as `` unusually densely knit '' subsets of a social network . This notion becomes more problematic if the social interactions change over time . Aggregating social networks over time can radically misrepresent the existing and changing community structure . Recently , we have proposed an optimization-based framework for modeling dynamic community structure . Also , we have proposed an algorithm for finding such structure based on maximum weight bipartite matching . In this paper , we analyze its performance guarantee for a special case where all actors can be observed at all times . In such instances , we show that the algorithm is a small constant factor approximation of the optimum . We use a similar idea to design an approximation algorithm for the general case where some individuals are possibly unobserved at times , and to show that the approximation factor increases twofold but remains a constant regardless of the input size . This is the first algorithm for inferring communities in dynamic networks with a provable approximation guarantee . We demonstrate the general algorithm on real data sets . The results confirm the efficiency and effectiveness of the algorithm in identifying dynamic communities .

O B I O O O O O O O B I O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O

approximation algorithms
dynamic social networks
general


Diversified ranking on large graphs : an optimization viewpoint Diversified ranking on graphs is a fundamental mining task and has a variety of high-impact applications . There are two important open questions here . The first challenge is the measure - how to quantify the goodness of a given top-k ranking list that captures both the relevance and the diversity ? The second challenge lies in the algorithmic aspect - how to find an optimal , or near-optimal , top-k ranking list that maximizes the measure we defined in a scalable way ? In this paper , we address these challenges from an optimization point of view . Firstly , we propose a goodness measure for a given top-k ranking list . The proposed goodness measure intuitively captures both ( a ) the relevance between each individual node in the ranking list and the query ; and ( b ) the diversity among different nodes in the ranking list . Moreover , we propose a scalable algorithm ( linear wrt the size of the graph ) that generates a provably near-optimal solution . The experimental evaluations on real graphs demonstrate its effectiveness and efficiency .

O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Structured learning for non-smooth ranking losses Learning to rank from relevance judgment is an active research area . Itemwise score regression , pairwise preference satisfaction , and listwise structured learning are the major techniques in use . Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC ( area under ROC curve ) and MAP ( mean average precision ) . We propose new , almost-linear-time algorithms to optimize for two other criteria widely used to evaluate search systems : MRR ( mean reciprocal rank ) and NDCG ( normalized discounted cumulative gain ) in the max-margin structured learning framework . We also demonstrate that , for different ranking criteria , one may need to use different feature maps . Search applications should not be optimized in favor of a single criterion , because they need to cater to a variety of queries . E.g. , MRR is best for navigational queries , while NDCG is best for informational queries . A key contribution of this paper is to fold multiple ranking loss functions into a multi-criteria max-margin optimization . The result is a single , robust ranking model that is close to the best accuracy of learners trained on individual criteria . In fact , experiments over the popular LETOR and TREC data sets show that , contrary to conventional wisdom , a test criterion is often not best served by training with the same individual criterion .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

max-margin structured learning to rank
non-decomposable loss functions


Cut-and-stitch : efficient parallel learning of linear dynamical systems on smps Multi-core processors with ever increasing number of cores per chip are becoming prevalent in modern parallel computing . Our goal is to make use of the multi-core as well as multi-processor architectures to speed up data mining algorithms . Specifically , we present a parallel algorithm for approximate learning of Linear Dynamical Systems ( LDS ) , also known as Kalman Filters ( KF ) . LDSs are widely used in time series analysis such as motion capture modeling , visual tracking etc. . We propose Cut-And-Stitch ( CAS ) , a novel method to handle the data dependencies from the chain structure of hidden variables in LDS , so as to parallelize the EM-based parameter learning algorithm . We implement the algorithm using OpenMP on both a supercomputer and a quad-core commercial desktop . The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the serial version . In addition , Cut-And-Stitch can be generalized to other models with similar linear structures such as Hidden Markov Models ( HMM ) and Switching Kalman Filters ( SKF ) .

O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O

kalman filters
linear dynamical systems
openmp

expectation maximization
multi-core
optimization


Event detection from evolution of click-through data Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data . In this paper , we propose the first approach to detect events from the click-through data , which is the log data of web search engines . The intuition behind event detection from click-through data is that such data is often event-driven and each event can be represented as a set ofquery-page pairs that are not only semantically similar but also have similar evolution pattern over time . Given the click-through data , in our proposed approach , we first segment it into a sequence of bipartite graphs based on theuser-defined time granularity . Next , the sequence of bipartite graphs is represented as a vector-based graph , which records the semantic and evolutionary relationships between queries and pages . After that , the vector-based graph is transformed into its dual graph , where each node is a query-page pair that will be used to represent real world events . Then , the problem of event detection is equivalent to the problem of clustering the dual graph of the vector-based graph . The clustering process is based on a two-phase graph cut algorithm . In the first phase , query-page pairs are clustered based on thesemantic-based similarity such that each cluster in the result corresponds to a specific topic . In the second phase , query-page pairs related to the same topic are further clustered based on the evolution pattern-based similarity such that each cluster is expected to represent a specific event under the specific topic . Experiments with real click-through data collected from a commercial web search engine show that the proposed approach produces high quality results .

B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Rule extraction from linear support vector machines We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that , unlike the original classifier , can be easily interpreted by humans . Each iteration of the rule extraction algorithm is formulated as a constrained optimization problem that is computationally inexpensive to solve . We discuss various properties of the algorithm and provide proof of convergence for two different optimization criteria We demonstrate the performance and the speed of the algorithm on linear classifiers learned from real-world datasets , including a medical dataset on detection of lung cancer from medical images . The ability to convert SVM 's and other `` black-box '' classifiers into a set of human-understandable rules , is critical not only for physician acceptance , but also to reducing the regulatory barrier for medical-decision support systems based on such classifiers .

B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

mathematical programming
medical decision-support
miscellaneous


Visually mining and monitoring massive time series Moments before the launch of every space vehicle , engineering discipline specialists must make a critical go\/no-go decision . The cost of a false positive , allowing a launch in spite of a fault , or a false negative , stopping a potentially successful launch , can be measured in the tens of millions of dollars , not including the cost in morale and other more intangible detriments . The Aerospace Corporation is responsible for providing engineering assessments critical to the go\/no-go decision for every Department of Defense space vehicle . These assessments are made by constantly monitoring streaming telemetry data in the hours before launch . We will introduce VizTree , a novel time-series visualization tool to aid the Aerospace analysts who must make these engineering assessments . VizTree was developed at the University of California , Riverside and is unique in that the same tool is used for mining archival data and monitoring incoming live telemetry . The use of a single tool for both aspects of the task allows a natural and intuitive transfer of mined knowledge to the monitoring task . Our visualization approach works by transforming the time series into a symbolic representation , and encoding the data in a modified suffix tree in which the frequency and other properties of patterns are mapped onto colors and other visual properties . We demonstrate the utility of our system by comparing it with state-of-the-art batch algorithms on several real and synthetic datasets .

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

anomaly detection
motif discovery
pattern discovery


Efficient elastic burst detection in data streams Burst detection is the activity of finding abnormal aggregates in data streams . Such aggregates are based on sliding windows over data streams . In some applications , we want to monitor many sliding window sizes simultaneously and to report those windows with aggregates significantly different from other periods . We will present a general data structure for detecting interesting aggregates over such elastic windows in near linear time . We present applications of the algorithm for detecting Gamma Ray Bursts in large-scale astrophysical data . Detection of periods with high volumes of trading activities and high stock price volatility is also demonstrated using real time Trade and Quote ( TAQ ) data from the New York Stock Exchange ( NYSE ) . Our algorithm beats the direct computation approach by several orders of magnitude .

O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Reasoning about sets using redescription mining Redescription mining is a newly introduced data mining problem that seeks to find subsets of data that afford multiple definitions . It can be viewed as a generalization of association rule mining , from finding implications to equivalences ; as a form of conceptual clustering , where the goal is to identify clusters that afford dual characterizations ; and as a form of constructive induction , to build features based on given descriptors that mutually reinforce each other . In this paper , we present the use of redescription mining as an important tool to reason about a collection of sets , especially their overlaps , similarities , and differences . We outline algorithms to mine all minimal ( non-redundant ) redescriptions underlying a dataset using notions of minimal generators of closed itemsets . We also show the use of these algorithms in an interactive context , supporting constraint-based exploration and querying . Specifically , we showcase a bioinformatics application that empowers the biologist to define a vocabulary of sets underlying a domain of genes and to reason about these sets , yielding significant biological insight .

O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

closed itemsets
minimal generators
redescription


Predictive client-side profiles for personalized advertising Personalization is ubiquitous in modern online applications as it provides significant improvements in user experience by adapting it to inferred user preferences . However , there are increasing concerns related to issues of privacy and control of the user data that is aggregated by online systems to power personalized experiences . These concerns are particularly significant for user profile aggregation in online advertising . This paper describes a practical , learning-driven client-side personalization approach for keyword advertising platforms , an emerging application previously not addressed in literature . Our approach relies on storing user-specific information entirely within the user 's control ( in a browser cookie or browser local storage ) , thus allowing the user to view , edit or purge it at any time ( e.g. , via a dedicated webpage ) . We develop a principled , utility-based formulation for the problem of iteratively updating user profiles stored client-side , which relies on calibrated prediction of future user activity . While optimal profile construction is NP-hard for pay-per-click advertising with bid increments , it can be efficiently solved via a greedy approximation algorithm guaranteed to provide a near-optimal solution due to the fact that keyword profile utility is submodular : it exhibits the property of diminishing returns with increasing profile size . We empirically evaluate client-side keyword profiles for keyword advertising on a large-scale dataset from a major search engine . Experiments demonstrate that predictive client-side personalization allows ad platforms to retain almost all of the revenue gains from personalization even if they give users the freedom to opt out of behavior tracking backed by server-side storage . Additionally , we show that advertisers can potentially increase their return on investment significantly by utilizing bid increments for keyword profiles in their ad campaigns .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


PVA : a self-adaptive personal view agent system In this paper , we present PVA , an adaptive personal view information agent system to track , learn and manage , user 's interests in Internet documents . When user 's interests change , PVA , in not only the contents , but also in the structure of user profile , is modified to adapt to the changes . Experimental results show that modulating the structure of user profile does increase the accuracy of personalization systems .

O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O

machine learning
systems and software
user interfaces
www


Efficient mining of iterative patterns for software specification discovery Studies have shown that program comprehension takes up to 45 % of software development costs . Such high costs are caused by the lack-of documented specification and further aggravated by the phenomenon of software evolution . There is a need for automated tools to extract specifications to aid program comprehension . In this paper , a novel technique to efficiently mine common software temporal patterns from traces is proposed . These patterns shed light on program behaviors , and are termed iterative patterns . They capture unique characteristic of software traces , typically not found in arbitrary sequences . Specifically , due to loops , interesting iterative patterns can occur multiple times within a trace . Furthermore , an occurrence of an iterative pattern in a trace can extend across a sequence of indefinite length . Since a program behavior can be manifested in numerous ways , analyzing a single trace will not be sufficient . Iterative pattern mining extends sequential pattern and episode minings to discover frequent iterative patterns which occur repetitively both within a program trace and across multiple traces . In this paper , we present CLIPER ( CLosed Iterative Pattern minER ) to efficiently mine a closed set of iterative patterns . A performance study on several simulated and real datasets shows the efficiency of our mining algorithm and effectiveness of our pruning strategy . Our case study on JBoss Application Server confirms the usefulness of mined patterns in discovering interesting software behavioral specification .

O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Support envelopes : a technique for exploring the structure of association patterns This paper introduces support envelopes -- a new tool for analyzing association patterns -- and illustrates some of their properties , applications , and possible extensions . Specifically , the support envelope for a transaction data set and a specified pair of positive integers ( m , n ) consists of the items and transactions that need to be searched to find any association pattern involving m or more transactions and n or more items . For any transaction data set with M transactions and N items , there is a unique lattice of at most M \* N support envelopes that captures the structure of the association patterns in that data set . Because support envelopes are not encumbered by a support threshold , this support lattice provides a complete view of the association structure of the data set , including association patterns that have low support . Furthermore , the boundary of the support lattice -- the support boundary -- has at most min ( M , N ) envelopes and is especially interesting since it bounds the maximum sizes of potential association patterns -- not only for frequent , closed , and maximal itemsets , but also for patterns , such as error-tolerant itemsets , that are more general . The association structure can be represented graphically as a two-dimensional scatter plot of the ( m , n ) values associated with the support envelopes of the data set , a feature that is useful in the exploratory analysis of association patterns . Finally , the algorithm to compute support envelopes is simple and computationally efficient , and it is straightforward to parallelize the process of finding all the support envelopes .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

association analysis
error-tolerant itemsets
formal concept analysis


Tracking multiple topics for finding interesting articles We introduce multiple topic tracking ( MTT ) for iScore to better recommend news articles for users with multiple interests and to address changes in user interests over time . As an extension of the basic Rocchio algorithm , traditional topic detection and tracking , and single-pass clustering , MTT maintains multiple interest profiles to identify interesting articles for a specific user given user-feedback . Focusing on only interesting topics enables iScore to discard useless profiles to address changes in user interests and to achieve a balance between resource consumption and classification accuracy . Also by relating a topic 's interestingness to an article . s interestingness , iScore is able to achieve higher quality results than traditional methods such as the Rocchio algorithm . We identify several operating parameters that work well for MTT . Using the same parameters , we show that MTT alone yields high quality results for recommending interesting articles from several corpora . The inclusion of MTT improves iScore 's performance by 9 % in recommending news articles from the Yahoo ! News RSS feeds and the TREC11 adaptive filter article collection . And through a small user study , we show that iScore can still perform well when only provided with little user feedback .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

content analysis and indexing
news filtering
news recommendation
personalization


A theoretical framework for learning from a pool of disparate data sources Many enterprises incorporate information gathered from a variety of data sources into an integrated input for some learning task . For example , aiming towards the design of an automated diagnostic tool for some disease , one may wish to integrate data gathered in many different hospitals . A major obstacle to such endeavors is that different data sources may vary considerably in the way they choose to represent related data . In practice , the problem is usually solved by a manual construction of semantic mappings and translations between the different sources . Recently there have been attempts to introduce automated algorithms based on machine learning tools for the construction of such translations . In this work we propose a theoretical framework for making classification predictions from a collection of different data sources , without creating explicit translations between them . Our framework allows a precise mathematical analysis of the complexity of such tasks , and it provides a tool for the development and comparison of different learning algorithms . Our main objective , at this stage , is to demonstrate the usefulness of computational learning theory to this practically important area and to stimulate further theoretical and experimental research of questions related to this framework .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Latent aspect rating analysis without aspect keyword supervision Mining detailed opinions buried in the vast amount of review text data is an important , yet quite challenging task with widespread applications in multiple domains . Latent Aspect Rating Analysis ( LARA ) refers to the task of inferring both opinion ratings on topical aspects ( e.g. , location , service of a hotel ) and the relative weights reviewers have placed on each aspect based on review content and the associated overall ratings . A major limitation of previous work on LARA is the assumption of pre-specified aspects by keywords . However , the aspect information is not always available , and it may be difficult to pre-define appropriate aspects without a good knowledge about what aspects are actually commented on in the reviews . In this paper , we propose a unified generative model for LARA , which does not need pre-specified aspect keywords and simultaneously mines 1 ) latent topical aspects , 2 ) ratings on each identified aspect , and 3 ) weights placed on different aspects by a reviewer . Experiment results on two different review data sets demonstrate that the proposed model can effectively perform the Latent Aspect Rating Analysis task without the supervision of aspect keywords . Because of its generality , the proposed model can be applied to explore all kinds of opinionated text data containing overall sentiment judgments and support a wide range of interesting application tasks , such as aspect-based opinion summarization , personalized entity ranking and recommendation , and reviewer behavior analysis .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

aspect identification
information search and retrieval
latent rating analysis
review mining


Evaluation of prediction models for marketing campaigns We consider prediction-model evaluation in the context of marketing-campaign planning . In order to evaluate and compare models with specific campaign objectives in mind , we need to concentrate our attention on the appropriate evaluation-criteria . These should portray the model 's ability to score accurately and to identify the relevant target population . In this paper we discuss some applicable model-evaluation and selection criteria , their relevance for campaign planning , their robustness under changing population distributions , and their employment when constructing confidence intervals . We illustrate our results with a case study based on our experience from several projects .

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O

confidence intervals
marketing campaigns


Mining top-k frequent items in a data stream with flexible sliding windows We study the problem of finding the k most frequent items in a stream of items for the recently proposed max-frequency measure . Based on the properties of an item , the max-frequency of an item is counted over a sliding window of which the length changes dynamically . Besides being parameterless , this way of measuring the support of items was shown to have the advantage of a faster detection of bursts in a stream , especially if the set of items is heterogeneous . The algorithm that was proposed for maintaining all frequent items , however , scales poorly when the number of items becomes large . Therefore , in this paper we propose , instead of reporting all frequent items , to only mine the top-k most frequent ones . First we prove that in order to solve this problem exactly , we still need a prohibitive amount of memory ( at least linear in the number of items ) . Yet , under some reasonable conditions , we show both theoretically and empirically that a memory-efficient algorithm exists . A prototype of this algorithm is implemented and we present its performance w.r.t. memory-efficiency on real-life data and in controlled experiments with synthetic data .


Improving predictions using aggregate information In domains such as consumer products or manufacturing amongst others , we have problems that warrant the prediction of a continuous target . Besides the usual set of explanatory attributes we may also have exact ( or approximate ) estimates of aggregated targets , which are the sums of disjoint sets of individual targets that we are trying to predict . Hence , the question now becomes can we use these aggregated targets , which are a coarser piece of information , to improve the quality of predictions of the individual targets ? In this paper , we provide a simple yet provable way of accomplishing this . In particular , given predictions from any regression model of the target on the test data , we elucidate a provable method for improving these predictions in terms of mean squared error , given exact ( or accurate enough ) information of the aggregated targets . These estimates of the aggregated targets may be readily available or obtained -- through multilevel regression -- at different levels of granularity . Based on the proof of our method we suggest a criterion for choosing the appropriate level . Moreover , in addition to estimates of the aggregated targets , if we have exact ( or approximate ) estimates of the mean and variance of the target distribution , then based on our general strategy we provide an optimal way of incorporating this information so as to further improve the quality of predictions of the individual targets . We then validate the results and our claims by conducting experiments on synthetic and real industrial data obtained from diverse domains .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Extracting discriminative concepts for domain adaptation in text mining One common predictive modeling challenge occurs in text mining problems is that the training data and the operational ( testing ) data are drawn from different underlying distributions . This poses a great difficulty for many statistical learning methods . However , when the distribution in the source domain and the target domain are not identical but related , there may exist a shared concept space to preserve the relation . Consequently a good feature representation can encode this concept space and minimize the distribution gap . To formalize this intuition , we propose a domain adaptation method that parameterizes this concept space by linear transformation under which we explicitly minimize the distribution difference between the source domain with sufficient labeled data and target domains with only unlabeled data , while at the same time minimizing the empirical loss on the labeled data in the source domain . Another characteristic of our method is its capability for considering multiple classes and their interactions simultaneously . We have conducted extensive experiments on two common text mining problems , namely , information extraction and document classification to demonstrate the effectiveness of our proposed method .

O O O O B I O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O


Understanding captions in biomedical publications From the standpoint of the automated extraction of scientific knowledge , an important but little-studied part of scientific publications are the figures and accompanying captions . Captions are dense in information , but also contain many extra-grammatical constructs , making them awkward to process with standard information extraction methods . We propose a scheme for `` understanding '' captions in biomedical publications by extracting and classifying `` image pointers '' ( references to the accompanying image ) . We evaluate a number of automated methods for this task , including hand-coded methods , methods based on existing learning techniques , and methods based on novel learning techniques . The best of these methods leads to a usefully accurate tool for caption-understanding , with both recall and precision in excess of 94 % on the most important single class in a combined extraction\/classification task .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

bioinformatics
boosting
information search and retrieval


Mining correlated bursty topic patterns from coordinated text streams Previous work on text mining has almost exclusively focused on a single stream . However , we often have available multiple text streams indexed by the same set of time points ( called coordinated text streams ) , which offer new opportunities for text mining . For example , when a major event happens , all the news articles published by different agencies in different languages tend to cover the same event for a certain period , exhibiting a correlated bursty topic pattern in all the news article streams . In general , mining correlated bursty topic patterns from coordinated text streams can reveal interesting latent associations or events behind these streams . In this paper , we define and study this novel text mining problem . We propose a general probabilistic algorithm which can effectively discover correlated bursty patterns and their bursty periods across text streams even if the streams have completely different vocabularies ( e.g. , English vs Chinese ) . Evaluation of the proposed method on a news data set and a literature data set shows that it can effectively discover quite meaningful topic patterns from both data sets : the patterns discovered from the news data set accurately reveal the major common events covered in the two streams of news articles ( in English and Chinese , respectively ) , while the patterns discovered from two database publication streams match well with the major research paradigm shifts in database research . Since the proposed method is general and does not require the streams to share vocabulary , it can be applied to any coordinated text streams to discover correlated topic patterns that burst in multiple streams in the same period .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Online allocation of display ads with smooth delivery Display ads on the Internet are often sold in bundles of thousands or millions of impressions over a particular time period , typically weeks or months . Ad serving systems that assign ads to pages on behalf of publishers must satisfy these contracts , but at the same time try to maximize overall quality of placement . This is usually modeled in the literature as an online allocation problem , where contracts are represented by overall delivery constraints over a finite time horizon . However this model misses an important aspect of ad delivery : time homogeneity . Advertisers who buy these packages expect their ad to be shown smoothly throughout the purchased time period , in order to reach a wider audience , to have a sustained impact , and to support the ads they are running on other media ( e.g. , television ) . In this paper we formalize this problem using several nested packing constraints , and develop a tight ( 1-1 \/ e ) - competitive online algorithm for this problem . Our algorithms and analysis require novel techniques as they involve online computation of multiple dual variables per ad . We then show the effectiveness of our algorithms through exhaustive simulation studies on real data sets .

O O O B I O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

ad allocation
general
nonnumerical algorithms and problems
online matching


Screening and interpreting multi-item associations based on log-linear modeling Association rules have received a lot of attention in the data mining community since their introduction . The classical approach to find rules whose items enjoy high support ( appear in a lot of the transactions in the data set ) is , however , filled with shortcomings . It has been shown that support can be misleading as an indicator of how interesting the rule is . Alternative measures , such as lift , have been proposed . More recently , a paper by DuMouchel et al. proposed the use of all-two-factor loglinear models to discover sets of items that can not be explained by pairwise associations between the items involved . This approach , however , has its limitations , since it stops short of considering higher order interactions ( other than pairwise ) among the items . In this paper , we propose a method that examines the parameters of the fitted loglinear models to find all the significant association patterns among the items . Since fitting loglinear models for large data sets can be computationally prohibitive , we apply graph-theoretical results to divide the original set of items into components ( sets of items ) that are statistically independent from each other . We then apply loglinear modeling to each of the components and find the interesting associations among items in them . The technique is experimentally evaluated with a real data set ( insurance data ) and a series of synthetic data sets . The results show that the technique is effective in finding interesting associations among the items involved .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

association rule
database applications
graphical model
log-linear model


Detecting privacy leaks using corpus-based association rules Detecting inferences in documents is critical for ensuring privacy when sharing information . In this paper , we propose a refined and practical model of inference detection using a reference corpus . Our model is inspired by association rule mining : inferences are based on word co-occurrences . Using the model and taking the Web as the reference corpus , we can find inferences and measure their strength through web-mining algorithms that leverage search engines such as Google or Yahoo ! . Our model also includes the important case of private corpora , to model inference detection in enterprise settings in which there is a large private document repository . We find inferences in private corpora by using analogs of our Web-mining algorithms , relying on an index for the corpus rather than a Web search engine . We present results from two experiments . The first experiment demonstrates the performance of our techniques in identifying all the keywords that allow for inference of a particular topic ( e.g. `` HIV '' ) with confidence above a certain threshold . The second experiment uses the public Enron e-mail dataset . We postulate a sensitive topic and use the Enron corpus and the Web together to find inferences for the topic . These experiments demonstrate that our techniques are practical , and that our model of inference based on word co-occurrence is well-suited to efficient inference detection .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O

association rule mining
inference detection
search engine


PET : a statistical model for popular events tracking in social communities User generated information in online communities has been characterized with the mixture of a text stream and a network structure both changing over time . A good example is a web-blogging community with the daily blog posts and a social network of bloggers . An important task of analyzing an online community is to observe and track the popular events , or topics that evolve over time in the community . Existing approaches usually focus on either the burstiness of topics or the evolution of networks , but ignoring the interplay between textual topics and network structures . In this paper , we formally define the problem of popular event tracking in online communities ( PET ) , focusing on the interplay between texts and networks . We propose a novel statistical method that models the the popularity of events over time , taking into consideration the burstiness of user interest , information diffusion on the network structure , and the evolution of textual topics . Specifically , a Gibbs Random Field is defined to model the influence of historic status and the dependency relationships in the graph ; thereafter a topic model generates the words in text content of the event , regularized by the Gibbs Random Field . We prove that two classic models in information diffusion and text burstiness are special cases of our model under certain situations . Empirical experiments with two different communities and datasets ( i.e. , Twitter and DBLP ) show that our approach is effective and outperforms existing approaches .

B O O O O O B I I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

pet
popular events tracking
social communities


Selection , combination , and evaluation of effective software sensors for detecting abnormal computer usage We present and empirically analyze a machine-learning approach for detecting intrusions on individual computers . Our Winnow-based algorithm continually monitors user and system behavior , recording such properties as the number of bytes transferred over the last 10 seconds , the programs that currently are running , and the load on the CPU . In all , hundreds of measurements are made and analyzed each second . Using this data , our algorithm creates a model that represents each particular computer 's range of normal behavior . Parameters that determine when an alarm should be raised , due to abnormal activity , are set on a per-computer basis , based on an analysis of training data . A major issue in intrusion-detection systems is the need for very low false-alarm rates . Our empirical results suggest that it is possible to obtain high intrusion-detection rates ( 95 % ) and low false-alarm rates ( less than one per day per computer ) , without `` stealing '' too many CPU cycles ( less than 1 % ) . We also report which system measurements are the most valuable in terms of detecting intrusions . A surprisingly large number of different measurements prove significantly useful .

anomaly detection
feature selection
intrusion detection
learning
machine learning
security and protection
user modeling
windows 2000
winnow algorithm