Attack detection in time series for recommender systems Recent research has identified significant vulnerabilities in recommender systems . Shilling attacks , in which attackers introduce biased ratings in order to influence future recommendations , have been shown to be effective against collaborative filtering algorithms . We postulate that the distribution of item ratings in time can reveal the presence of a wide range of shilling attacks given reasonable assumptions about their duration . To construct a time series of ratings for an item , we use a window size of k to group consecutive ratings for the item into disjoint windows and compute the sample average and sample entropy in each window . We derive a theoretically optimal window size to best detect an attack event if the number of attack profiles is known . For practical applications where this number is unknown , we propose a heuristic algorithm that adaptively changes the window size . Our experimental results demonstrate that monitoring rating distributions in time series is an effective approach for detecting shilling attacks .

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

recommender systems
shilling attacks
time series


IDR\/QR : an incremental dimension reduction algorithm via QR decomposition Dimension reduction is critical for many database and data mining applications , such as efficient storage and retrieval of high-dimensional data . In the literature , a well-known dimension reduction scheme is Linear Discriminant Analysis ( LDA ) . The common aspect of previously proposed LDA based algorithms is the use of Singular Value Decomposition ( SVD ) . Due to the difficulty of designing an incremental solution for the eigenvalue problem on the product of scatter matrices in LDA , there is little work on designing incremental LDA algorithms . In this paper , we propose an LDA based incremental dimension reduction algorithm , called IDR\/QR , which applies QR Decomposition rather than SVD . Unlike other LDA based algorithms , this algorithm does not require the whole data matrix in main memory . This is desirable for large data sets . More importantly , with the insertion of new data items , the IDR\/QR algorithm can constrain the computational cost by applying efficient QR-updating techniques . Finally , we evaluate the effectiveness of the IDR\/QR algorithm in terms of classification accuracy on the reduced dimensional space . Our experiments on several real-world data sets reveal that the accuracy achieved by the IDR\/QR algorithm is very close to the best possible accuracy achieved by other LDA based algorithms . However , the IDR\/QR algorithm has much less computational cost , especially when new data items are dynamically inserted .

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

dimension reduction
linear discriminant analysis
qr decomposition


Playing hide-and-seek with correlations We present a method for very high-dimensional correlation analysis . The method relies equally on rigorous search strategies and on human interaction . At each step , the method conservatively `` shaves off '' a fraction of the database tuples and attributes , so that most of the correlations present in the data are not affected by the decomposition . Instead , the correlations become more obvious to the user , because they are hidden in a much smaller portion of the database . This process can be repeated iteratively and interactively , until only the most important correlations remain . The main technical difficulty of the approach is figuring out how to `` shave off '' part of the database so as to preserve most correlations . We develop an algorithm for this problem that has a polynomial running time and guarantees result quality .

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

association rules
data mining
database applications
minimum cut


Simple and effective visual models for gene expression cancer diagnostics In the paper we show that diagnostic classes in cancer gene expression data sets , which most often include thousands of features ( genes ) , may be effectively separated with simple two-dimensional plots such as scatterplot and radviz graph . The principal innovation proposed in the paper is a method called VizRank , which is able to score and identify the best among possibly millions of candidate projections for visualizations . Compared to recently much applied techniques in the field of cancer genomics that include neural networks , support vector machines and various ensemble-based approaches , VizRank is fast and finds visualization models that can be easily examined and interpreted by domain experts . Our experiments on a number of gene expression data sets show that VizRank was always able to find data visualizations with a small number of ( two to seven ) genes and excellent class separation . In addition to providing grounds for gene expression cancer diagnosis , VizRank and its visualizations also identify small sets of relevant genes , uncover interesting gene interactions and point to outliers and potential misclassifications in cancer data sets .

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

data mining
data visualization
gene expression analysis
machine learning


Extracting temporal signatures for comprehending systems biology models Systems biology has made massive strides in recent years , with capabilities to model complex systems including cell division , stress response , energy metabolism , and signaling pathways . Concomitant with their improved modeling capabilities , however , such biochemical network models have also become notoriously complex for humans to comprehend . We propose network comprehension as a key problem for the KDD community , where the goal is to create explainable representations of complex biological networks . We formulate this problem as one of extracting temporal signatures from multi-variate time series data , where the signatures are composed of ordinal comparisons between time series components . We show how such signatures can be inferred by formulating the data mining problem as one of feature selection in rank-order space . We propose five new feature selection strategies for rank-order space and assess their selective superiorities . Experimental results on budding yeast cell cycle models demonstrate compelling results comparable to human interpretations of the cell cycle .

O B I 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 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 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

biological networks
feature selection
systems biology
temporal signatures


Identifying biologically relevant genes via multiple heterogeneous data sources Selection of genes that are differentially expressed and critical to a particular biological process has been a major challenge in post-array analysis . Recent development in bioinformatics has made various data sources available such as mRNA and miRNA expression profiles , biological pathway and gene annotation , etc. . Efficient and effective integration of multiple data sources helps enrich our knowledge about the involved samples and genes for selecting genes bearing significant biological relevance . In this work , we studied a novel problem of multi-source gene selection : given multiple heterogeneous data sources ( or data sets ) , select genes from expression profiles by integrating information from various data sources . We investigated how to effectively employ information contained in multiple data sources to extract an intrinsic global geometric pattern and use it in covariance analysis for gene selection . We designed and conducted experiments to systematically compare the proposed approach with representative methods in terms of statistical and biological significance , and showed the efficacy and potential of the proposed approach with promising findings .

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


Partial example acquisition in cost-sensitive learning It is often expensive to acquire data in real-world data mining applications . Most previous data mining and machine learning research , however , assumes that a fixed set of training examples is given . In this paper , we propose an online cost-sensitive framework that allows a learner to dynamically acquire examples as it learns , and to decide the ideal number of examples needed to minimize the total cost . We also propose a new strategy for Partial Example Acquisition ( PAS ) , in which the learner can acquire examples with a subset of attribute values to reduce the data acquisition cost . Experiments on UCI datasets show that the new PAS strategy is an effective method in reducing the total cost for data acquisition .

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

data acquisition
data mining
machine learning

active cost-sensitive learning
active learning
cost-sensitive learning
interactive and online data mining


Prominent streak discovery in sequence data This paper studies the problem of prominent streak discovery in sequence data . Given a sequence of values , a prominent streak is a long consecutive subsequence consisting of only large ( small ) values . For finding prominent streaks , we make the observation that prominent streaks are skyline points in two dimensions - streak interval length and minimum value in the interval . Our solution thus hinges upon the idea to separate the two steps in prominent streak discovery ' candidate streak generation and skyline operation over candidate streaks . For candidate generation , we propose the concept of local prominent streak ( LPS ) . We prove that prominent streaks are a subset of LPSs and the number of LPSs is less than the length of a data sequence , in comparison with the quadratic number of candidates produced by a brute-force baseline method . We develop efficient algorithms based on the concept of LPS . The non-linear LPS-based method ( NLPS ) considers a superset of LPSs as candidates , and the linear LPS-based method ( LLPS ) further guarantees to consider only LPSs . The results of experiments using multiple real datasets verified the effectiveness of the proposed methods and showed orders of magnitude performance improvement against the baseline method .

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

sequence database
skyline query
time-series database


Mining sequential patterns from probabilistic databases We consider sequential pattern mining in situations where there is uncertainty about which source an event is associated with . We model this in the probabilistic database framework and consider the problem of enumerating all sequences whose expected support is sufficiently large . Unlike frequent itemset mining in probabilistic databases ( C. Aggarwal et al. . KDD ' 09 ; Chui et al. , PAKDD ' 07 ; Chui and Kao , PAKDD ' 08 ) , we use dynamic programming ( DP ) to compute the probability that a source supports a sequence , and show that this suffices to compute the expected support of a sequential pattern . Next , we embed this DP algorithm into candidate generate-and-test approaches , and explore the pattern lattice both in a breadth-first ( similar to GSP ) and a depth-first ( similar to SPAM ) manner . We propose optimizations for efficiently computing the frequent 1-sequences , for re-using previously-computed results through incremental support computation , and for elmiminating candidate sequences without computing their support via probabilistic pruning . Preliminary experiments show that our optimizations are effective in improving the CPU cost .

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 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

mining complex sequential data
mining uncertain data
novel models and algorithms


Cross-training : learning probabilistic mappings between topics Classification is a well-established operation in text mining . Given a set of labels A and a set DA of training documents tagged with these labels , a classifier learns to assign labels to unlabeled test documents . Suppose we also had available a different set of labels B , together with a set of documents DB marked with labels from B. If A and B have some semantic overlap , can the availability of DB help us build a better classifier for A , and vice versa ? We answer this question in the affirmative by proposing cross-training : a new approach to semi-supervised learning in presence of multiple label sets . We give distributional and discriminative algorithms for cross-training and show , through extensive experiments , that cross-training can discover and exploit probabilistic relations between two taxonomies for more accurate classification .

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 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

design methodology
document classification
em
semi-supervised multi-task learning
support vector machines


Mining GPS data to augment road models

evaluating knowledge and potential discoveries


Evaluating similarity measures : a large-scale study in the orkut social network Online information services have grown too large for users to navigate without the help of automated tools such as collaborative filtering , which makes recommendations to users based on their collective past behavior . While many similarity measures have been proposed and individually evaluated , they have not been evaluated relative to each other in a large real-world environment . We present an extensive empirical comparison of six distinct measures of similarity for recommending online communities to members of the Orkut social network . We determine the usefulness of the different recommendations by actually measuring users ' propensity to visit and join recommended communities . We also examine how the ordering of recommendations influenced user selection , as well as interesting social issues that arise in recommending communities within a real social network .

O O O O O O O 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 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

collaborative filtering
online communities

on-line information services
recommender system
similarity measure
social networks


Clustering event logs using iterative partitioning The importance of event logs , as a source of information in systems and network management can not be overemphasized . With the ever increasing size and complexity of today 's event logs , the task of analyzing event logs has become cumbersome to carry out manually . For this reason recent research has focused on the automatic analysis of these log files . In this paper we present IPLoM ( Iterative Partitioning Log Mining ) , a novel algorithm for the mining of clusters from event logs . Through a 3-Step hierarchical partitioning process IPLoM partitions log data into its respective clusters . In its 4th and final stage IPLoM produces cluster descriptions or line formats for each of the clusters produced . Unlike other similar algorithms IPLoM is not based on the Apriori algorithm and it is able to find clusters in data whether or not its instances appear frequently . Evaluations show that IPLoM outperforms the other algorithms statistically significantly , and it is also able to achieve an average F-Measure performance 78 % when the closest other algorithm achieves an F-Measure performance of 10 % .

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

event log mining
fault management
telecommunications


Data mining solves tough semiconductor manufacturing problems

machine learning
manufacturing optimization
neural networks
pattern recognition
rule induction
self organizing maps
semiconductor yield enhancement


Visualizing changes in the structure of data for exploratory feature selection Using visualization techniques to explore and understand high-dimensional data is an efficient way to combine human intelligence with the immense brute force computation power available nowadays . Several visualization techniques have been developed to study the cluster structure of data , i.e. , the existence of distinctive groups in the data and how these clusters are related to each other . However , only few of these techniques lend themselves to studying how this structure changes if the features describing the data are changed . Understanding this relationship between the features and the cluster structure means understanding the features themselves and is thus a useful tool in the feature extraction phase . In this paper we present a novel approach to visualizing how modification of the features with respect to weighting or normalization changes the cluster structure . We demonstrate the application of our approach in two music related data mining projects .

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

high-dimensional data
interactive data mining


Knowledge transfer via multiple model local structure mapping The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from which test examples are to be drawn . The task can be especially difficult when the training examples are from one or several domains different from the test domain . In this paper , we propose a locally weighted ensemble framework to combine multiple models for transfer learning , where the weights are dynamically assigned according to a model 's predictive power on each test example . It can integrate the advantages of various learning algorithms and the labeled information from multiple training domains into one unified classification model , which can then be applied on a different domain . Importantly , different from many previously proposed methods , none of the base learning method is required to be specifically designed for transfer learning . We show the optimality of a locally weighted ensemble framework as a general approach to combine multiple models for domain transfer . We then propose an implementation of the local weight assignments by mapping the structures of a model onto the structures of the test domain , and then weighting each model locally according to its consistency with the neighborhood structure around the test example . Experimental results on text classification , spam filtering and intrusion detection data sets demonstrate significant improvements in classification accuracy gained by the framework . On a transfer learning task of newsgroup message categorization , the proposed locally weighted ensemble framework achieves 97 % accuracy when the best single model predicts correctly only on 73 % of the test examples . In summary , the improvement in accuracy is over 10 % and up to 30 % across different problems .

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 B 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 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 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 B O O O 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 O O O O 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
ensemble
transfer learning


CVS : a Correlation-Verification based Smoothing technique on information retrieval and term clustering As information volume in enterprise systems and in the Web grows rapidly , how to accurately retrieve information is an important research area . Several corpus based smoothing techniques have been proposed to address the data sparsity and synonym problems faced by information retrieval systems . Such smoothing techniques are often unable to discover and utilize the correlations among terms . We propose CVS , a Correlation-Verification based Smoothing method , that considers co-occurrence information in smoothing . Strongly correlated terms in a document are identified by their co-occurrence frequencies in the document . To avoid missing correlated terms with low co-occurrence frequencies but specific to the theme of the document , the joint distributions of terms in the document are compared with those in the corpus for statistical significance . A common approach to apply corpus based smoothing techniques to information retrieval is by refining the vector representations of documents . This paper investigates the effects of corpus based smoothing on information retrieval by query expansion using term clusters generated from a term clustering process . The results can also be viewed in light of the effects of smoothing on clustering . Empirical studies show that our approach outperforms previous corpus based smoothing techniques . It improves retrieval effectiveness by 14.6 % . The results demonstrate that corpus based smoothing can be used for query expansion by term clustering .

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 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 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 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 B I O B I O

information retrieval
query expansion
term clustering

content analysis and indexing
text mining


Land cover change detection : a case study The study of land cover change is an important problem in the Earth Science domain because of its impacts on local climate , radiation balance , biogeochemistry , hydrology , and the diversity and abundance of terrestrial species . Most well-known change detection techniques from statistics , signal processing and control theory are not well-suited for the massive high-dimensional spatio-temporal data sets from Earth Science due to limitations such as high computational complexity and the inability to take advantage of seasonality and spatio-temporal autocorrelation inherent in Earth Science data . In our work , we seek to address these challenges with new change detection techniques that are based on data mining approaches . Specifically , in this paper we have performed a case study for a new change detection technique for the land cover change detection problem . We study land cover change in the state of California , focusing on the San Francisco Bay Area and perform an extended study on the entire state . We also perform a comparative evaluation on forests in the entire state . These results demonstrate the utility of data mining techniques for the land cover change detection problem .

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


Extracting relevant named entities for automated expense reimbursement Expense reimbursement is a time-consuming and labor-intensive process across organizations . In this paper , we present a prototype expense reimbursement system that dramatically reduces the elapsed time and costs involved , by eliminating paper from the process life cycle . Our complete solution involves ( 1 ) an electronic submission infrastructure that provides multi - channel image capture , secure transport and centralized storage of paper documents ; ( 2 ) an unconstrained data mining approach to extracting relevant named entities from un-structured document images ; ( 3 ) automation of auditing procedures that enables automatic expense validation with minimum human interaction . Extracting relevant named entities robustly from document images with unconstrained layouts and diverse formatting is a fundamental technical challenge to image-based data mining , question answering , and other information retrieval tasks . In many applications that require such capability , applying traditional language modeling techniques to the stream of OCR text does not give satisfactory result due to the absence of linguistic context . We present an approach for extracting relevant named entities from document images by combining rich page layout features in the image space with language content in the OCR text using a discriminative conditional random field ( CRF ) framework . We integrate this named entity extraction engine into our expense reimbursement solution and evaluate the system performance on large collections of real-world receipt images provided by IBM World Wide Reimbursement Center .

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

conditional random fields
document layout analysis
general
learning
optical character recognition


Mining broad latent query aspects from search sessions Search queries are typically very short , which means they are often underspecified or have senses that the user did not think of . A broad latent query aspect is a set of keywords that succinctly represents one particular sense , or one particular information need , that can aid users in reformulating such queries . We extract such broad latent aspects from query reformulations found in historical search session logs . We propose a framework under which the problem of extracting such broad latent aspects reduces to that of optimizing a formal objective function under constraints on the total number of aspects the system can store , and the number of aspects that can be shown in response to any given query . We present algorithms to find a good set of aspects , and also to pick the best k aspects matching any query . Empirical results on real-world search engine logs show significant gains over a strong baseline that uses single-keyword reformulations : a gain of 14 % and 23 % in terms of human-judged accuracy and click-through data respectively , and around 20 % in terms of consistency among aspects predicted for `` similar '' queries . This demonstrates both the importance of broad query aspects , and the efficacy of our algorithms for extracting them .

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


Combining proactive and reactive predictions for data streams Mining data streams is important in both science and commerce . Two major challenges are ( 1 ) the data may grow without limit so that it is difficult to retain a long history ; and ( 2 ) the underlying concept of the data may change over time . Different from common practice that keeps recent raw data , this paper uses a measure of conceptual equivalence to organize the data history into a history of concepts . Along the journey of concept change , it identifies new concepts as well as re-appearing ones , and learns transition patterns among concepts to help prediction . Different from conventional methodology that passively waits until the concept changes , this paper incorporates proactive and reactive predictions . In a proactive mode , it anticipates what the new concept will be if a future concept change takes place , and prepares prediction strategies in advance . If the anticipation turns out to be correct , a proper prediction model can be launched instantly upon the concept change . If not , it promptly resorts to a reactive mode : adapting a prediction model to the new data . A system RePro is proposed to implement these new ideas . Experiments compare the system with representative existing prediction methods on various benchmark data sets that represent diversified scenarios of concept change . Empirical evidence demonstrates that the proposed methodology is an effective and efficient solution to prediction for data streams .

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


Constraint-driven clustering Clustering methods can be either data-driven or need-driven . Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at organizing the true structure to meet certain application requirements . Thus , need-driven ( e.g. constrained ) clustering is able to find more useful and actionable clusters in applications such as energy aware sensor networks , privacy preservation , and market segmentation . However , the existing methods of constrained clustering require users to provide the number of clusters , which is often unknown in advance , but has a crucial impact on the clustering result . In this paper , we argue that a more natural way to generate actionable clusters is to let the application-specific constraints decide the number of clusters . For this purpose , we introduce a novel cluster model , Constraint-Driven Clustering ( CDC ) , which finds an a priori unspecified number of compact clusters that satisfy all user-provided constraints . Two general types of constraints are considered , i.e. minimum significance constraints and minimum variance constraints , as well as combinations of these two types . We prove the NP-hardness of the CDC problem with different constraints . We propose a novel dynamic data structure , the CD-Tree , which organizes data points in leaf nodes such that each leaf node approximately satisfies the CDC constraints and minimizes the objective function . Based on CD-Trees , we develop an efficient algorithm to solve the new clustering problem . Our experimental evaluation on synthetic and real datasets demonstrates the quality of the generated clusters and the scalability of the algorithm .

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


Clustering spatial data using random walks Discovering significant patterns that exist implicitly in huge spatial databases is an important computational task . A common approach to this problem is to use cluster analysis . We propose a novel approach to clustering , based on the deterministic analysis of random walks on a weighted graph generated from the data . Our approach can decompose the data into arbitrarily shaped clusters of different sizes and densities , overcoming noise and outliers that may blur the natural decomposition of the data . The method requires only O ( n log n ) time , and one of its variants needs only constant space .

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


Improving classification accuracy using automatically extracted training data Classification is a core task in knowledge discovery and data mining , and there has been substantial research effort in developing sophisticated classification models . In a parallel thread , recent work from the NLP community suggests that for tasks such as natural language disambiguation even a simple algorithm can outperform a sophisticated one , if it is provided with large quantities of high quality training data . In those applications , training data occurs naturally in text corpora , and high quality training data sets running into billions of words have been reportedly used . We explore how we can apply the lessons from the NLP community to KDD tasks . Specifically , we investigate how to identify data sources that can yield training data at low cost and study whether the quantity of the automatically extracted training data can compensate for its lower quality . We carry out this investigation for the specific task of inferring whether a search query has commercial intent . We mine toolbar and click logs to extract queries from sites that are predominantly commercial ( e.g. , Amazon ) and non-commercial ( e.g. , Wikipedia ) . We compare the accuracy obtained using such training data against manually labeled training data . Our results show that we can have large accuracy gains using automatically extracted training data at much lower cost .

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


Applying data mining techniques to address disaster information management challenges on mobile devices The improvement of Crisis Management and Disaster Recovery techniques are national priorities in the wake of man-made and nature inflicted calamities of the last decade . Our prior work has demonstrated that the efficiency of sharing and managing information plays an important role in business recovery efforts after disaster event . With the proliferation of smart phones and wireless tablets , professionals who have an operational responsibility in disaster situations are relying on such devices to maintain communication . Further , with the rise of social media , technology savvy consumers are also using these devices extensively for situational updates . In this paper , we address several critical tasks which can facilitate information sharing and collaboration between both private and public sector participants for major disaster recovery planning and management . We design and implement an All-Hazard Disaster Situation Browser ( ADSB ) system that runs on Apple 's mobile operating system ( iOS ) and iPhone and iPad mobile devices . Our proposed techniques create a collaborative solution on a mobile platform using advanced data mining and information retrieval techniques for disaster preparedness and recovery that helps impacted communities better understand the current disaster situation and how the community is recovering . Specifically , hierarchical summarization techniques are used to generate brief reviews from a large collection of reports at different granularities ; probabilistic models are proposed to dynamically generate query forms based on user 's feedback ; and recommendation techniques are adapted to help users identify potential contacts for report sharing and community organization . Furthermore , the developed techniques are designed to be all-hazard capable so that they can be used in earthquake , terrorism , or other unanticipated disaster situations .

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 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

disaster information management
hierarchical summarization


Adversarial learning Many classification tasks , such as spam filtering , intrusion detection , and terrorism detection , are complicated by an adversary who wishes to avoid detection . Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier ( 2 ) . In this paper , we introduce the adversarial classifier reverse engineering ( ACRE ) learning problem , the task of learning sufficient information about a classifier to construct adversarial attacks . We present efficient algorithms for reverse engineering linear classifiers with either continuous or Boolean features and demonstrate their effectiveness using real data from the domain of spam filtering .

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

adversarial classification
linear classifiers
spam


Smoothing techniques for adaptive online language models : topic tracking in tweet streams We are interested in the problem of tracking broad topics such as `` baseball '' and `` fashion '' in continuous streams of short texts , exemplified by tweets from the microblogging service Twitter . The task is conceived as a language modeling problem where per-topic models are trained using hashtags in the tweet stream , which serve as proxies for topic labels . Simple perplexity-based classifiers are then applied to filter the tweet stream for topics of interest . Within this framework , we evaluate , both intrinsically and extrinsically , smoothing techniques for integrating `` foreground '' models ( to capture recency ) and `` background '' models ( to combat sparsity ) , as well as different techniques for retaining history . Experiments show that unigram language models smoothed using a normalized extension of stupid backoff and a simple queue for history retention performs well on the 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 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


A multinomial clustering model for fast simulation of computer architecture designs Computer architects utilize simulation tools to evaluate the merits of a new design feature . The time needed to adequately evaluate the tradeoffs associated with adding any new feature has become a critical issue . Recent work has found that by identifying execution phases present in common workloads used in simulation studies , we can apply clustering algorithms to significantly reduce the amount of time needed to complete the simulation . Our goal in this paper is to demonstrate the value of this approach when applied to the set of industry-standard benchmarks most commonly used in computer architecture studies . We also look to improve upon prior work by applying more appropriate clustering algorithms to identify phases , and to further reduce simulation time . We find that the phase clustering in computer architecture simulation has many similarities to text clustering . In prior work on clustering techniques to reduce simulation time , K-means clustering was used to identify representative program phases . In this paper we apply a mixture of multinomials to the clustering problem and show its advantages over using K-means on simulation data . We have implemented these two clustering algorithms and evaluate how well they can characterize program behavior . By adopting a mixture of multinomials model , we find that we can maintain simulation result fidelity , while greatly reducing overall simulation time . We report results for a range of applications taken from the SPEC2000 benchmark suite .

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

clustering
mixture of multinomials
simulation

em
k-means
program phase
single data stream architectures


Outlier detection by sampling with accuracy guarantees An effective approach to detecting anomalous points in a data set is distance-based outlier detection . This paper describes a simple sampling algorithm to effciently detect distance-based outliers in domains where each and every distance computation is very expensive . Unlike any existing algorithms , the sampling algorithm requires a xed number of distance computations and can return good results with accuracy guarantees . The most computationally expensive aspect of estimating the accuracy of the result is sorting all of the distances computed by the sampling algorithm . The experimental study on two expensive domains as well as ten additional real-life datasets demonstrates both the effciency and effectiveness of the sampling algorithm in comparison with the state-of-the-art algorithm and there liability of the accuracy guarantees .

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


Automatic labeling of multinomial topic models Multinomial distributions over words are frequently used to model topics in text collections . A common , major challenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can interpret the discovered topic . So far , such labels have been generated manually in a subjective way . In this paper , we propose probabilistic approaches to automatically labeling multinomial topic models in an objective way . We cast this labeling problem as an optimization problem involving minimizing Kullback-Leibler divergence between word distributions and maximizing mutual information between a label and a topic model . Experiments with user study have been done on two text data sets with different genres . The results show that the proposed labeling methods are quite effective to generate labels that are meaningful and useful for interpreting the discovered topic models . Our methods are general and can be applied to labeling topics learned through all kinds of topic models such as PLSA , LDA , and their variations .

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

information search and retrieval
multinomial distribution
statistical topic models
topic model labeling


Activity monitoring : noticing interesting changes in behavior

database administration
database applications
security and protection


Privacy-preserving cox regression for survival analysis Privacy-preserving data mining ( PPDM ) is an emergent research area that addresses the incorporation of privacy preserving concerns to data mining techniques . In this paper we propose a privacy-preserving ( PP ) Cox model for survival analysis , and consider a real clinical setting where the data is horizontally distributed among different institutions . The proposed model is based on linearly projecting the data to a lower dimensional space through an optimal mapping obtained by solving a linear programming problem . Our approach differs from the commonly used random projection approach since it instead finds a projection that is optimal at preserving the properties of the data that are important for the specific problem at hand . Since our proposed approach produces an sparse mapping , it also generates a PP mapping that not only projects the data to a lower dimensional space but it also depends on a smaller subset of the original features ( it provides explicit feature selection ) . Real data from several European healthcare institutions are used to test our model for survival prediction of non-small-cell lung cancer patients . These results are also confirmed using publicly available benchmark datasets . Our experimental results show that we are able to achieve a near-optimal performance without directly sharing the data across different data sources . This model makes it possible to conduct large-scale multi-centric survival analysis without violating privacy-preserving requirements .

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 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 B I O O O O O

miscellaneous
privacy-preserving data mining


Interpretable nonnegative matrix decompositions A matrix decomposition expresses a matrix as a product of at least two factor matrices . Equivalently , it expresses each column of the input matrix as a linear combination of the columns in the first factor matrix . The interpretability of the decompositions is a key issue in many data-analysis tasks . We propose two new matrix-decomposition problems : the nonnegative CX and nonnegative CUR problems , that give naturally interpretable factors . They extend the recently-proposed column and column-row based decompositions , and are aimed to be used with nonnegative matrices . Our decompositions represent the input matrix as a nonnegative linear combination of a subset of its columns ( or columns and rows ) . We present two algorithms to solve these problems and provide an extensive experimental evaluation where we assess the quality of our algorithms ' results as well as the intuitiveness of nonnegative CX and CUR decompositions . We show that our algorithms return intuitive answers with smaller reconstruction errors than the previously-proposed methods for column and column-row decompositions .

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

alternating least squares
column-row decompositions
local search


On demand classification of data streams Current models of the classification problem do not effectively handle bursts of particular classes coming in at different times . In fact , the current model of the classification problem simply concentrates on methods for one-pass classification modeling of very large data sets . Our model for data stream classification views the data stream classification problem from the point of view of a dynamic approach in which simultaneous training and testing streams are used for dynamic classification of data sets . This model reflects real life situations effectively , since it is desirable to classify test streams in real time over an evolving training and test stream . The aim here is to create a classification system in which the training model can adapt quickly to the changes of the underlying data stream . In order to achieve this goal , we propose an on-demand classification process which can dynamically select the appropriate window of past training data to build the classifier . The empirical results indicate that the system maintains a high classification accuracy in an evolving data stream , while providing an efficient solution to the classification task .

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


Mining e-commerce data : the good , the bad , and the ugly Organizations conducting Electronic Commerce ( e-commerce ) can greatly benefit from the insight that data mining of transactional and clickstream data provides . Such insight helps not only to improve the electronic channel ( e.g. , a web site ) , but it is also a learning vehicle for the bigger organization conducting business at brick-and-mortar stores . The e-commerce site serves as an early alert system for emerging patterns and a laboratory for experimentation . For successful data mining , several ingredients are needed and e-commerce provides all the right ones ( the Good ) . Web server logs , which are commonly used as the source of data for mining e-commerce data , were designed to debug web servers , and the data they provide is insufficient , requiring the use of heuristics to reconstruct events . Moreover , many events are never logged in web server logs , limiting the source of data for mining ( the Bad ) . Many of the problems of dealing with web server log data can be resolved by properly architecting the e-commerce sites to generate data needed for mining . Even with a good architecture , however , there are challenging problems that remain hard to solve ( the Ugly ) . Lessons and metrics based on mining real e-commerce data are presented .

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

application server
e-commerce
web site architecture


Summarizing itemset patterns : a profile-based approach Frequent-pattern mining has been studied extensively on scalable methods for mining various kinds of patterns including itemsets , sequences , and graphs . However , the bottleneck of frequent-pattern mining is not at the efficiency but at the interpretability , due to the huge number of patterns generated by the mining process . In this paper , we examine how to summarize a collection of itemset patterns using only K representatives , a small number of patterns that a user can handle easily . The K representatives should not only cover most of the frequent patterns but also approximate their supports . A generative model is built to extract and profile these representatives , under which the supports of the patterns can be easily recovered without consulting the original dataset . Based on the restoration error , we propose a quality measure function to determine the optimal value of parameter K. Polynomial time algorithms are developed together with several optimization heuristics for efficiency improvement . Empirical studies indicate that we can obtain compact summarization in real datasets .

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


Efficient and effective explanation of change in hierarchical summaries Dimension attributes in data warehouses are typically hierarchical ( e.g. , geographic locations in sales data , URLs in Web traffic logs ) . OLAP tools are used to summarize the measure attributes ( e.g. , total sales ) along a dimension hierarchy , and to characterize changes ( e.g. , trends and anomalies ) in a hierarchical summary over time . When thenumber of changes identified is large ( e.g. , total sales in many stores differed from their expected values ) , a parsimonious explanation of the most significant changes is desirable . In this paper , we propose a natural model of parsimonious explanation , as a composition of node weights along the root-to-leaf paths in a dimension hierarchy , which permits changes to be aggregated with maximal generalization along the dimension hierarchy . We formalize this model of explaining changes in hierarchical summaries and investigate the problem of identifying optimally parsimonious explanations on arbitrary rooted one dimensional tree hierarchies . We show that such explanations can be computed efficiently in time essentially proportional to the number of leaves and the depth of the hierarchy . Further , our method can produce parsimonious explanations from the output of any statistical model that provides predictions and confidence intervals , making it widely applicable . Our experiments use real data sets to demonstrate the utility and robustness of our proposed model for explaining significant changes , as well as its superior parsimony compared to alternatives .

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 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 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 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 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

change
hierarchical summary
olap
parsimonious explanations
statistical model


Mining reference tables for automatic text segmentation Automatically segmenting unstructured text strings into structured records is necessary for importing the information contained in legacy sources and text collections into a data warehouse for subsequent querying , analysis , mining and integration . In this paper , we mine tables present in data warehouses and relational databases to develop an automatic segmentation system . Thus , we overcome limitations of existing supervised text segmentation approaches , which require comprehensive manually labeled training data . Our segmentation system is robust , accurate , and efficient , and requires no additional manual effort . Thorough evaluation on real datasets demonstrates the robustness and accuracy of our system , with segmentation accuracy exceeding state of the art supervised approaches .

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 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

data cleaning
information extraction
learning
machine learning
text management


Growing a tree in the forest : constructing folksonomies by integrating structured metadata Many social Web sites allow users to annotate the content with descriptive metadata , such as tags , and more recently to organize content hierarchically . These types of structured metadata provide valuable evidence for learning how a community organizes knowledge . For instance , we can aggregate many personal hierarchies into a common taxonomy , also known as a folksonomy , that will aid users in visualizing and browsing social content , and also to help them in organizing their own content . However , learning from social metadata presents several challenges , since it is sparse , shallow , ambiguous , noisy , and inconsistent . We describe an approach to folksonomy learning based on relational clustering , which exploits structured metadata contained in personal hierarchies . Our approach clusters similar hierarchies using their structure and tag statistics , then incrementally weaves them into a deeper , bushier tree . We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr , and demonstrate that the proposed approach addresses the challenges . Moreover , comparing to previous work , the approach produces larger , more accurate folksonomies , and in addition , scales better .

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 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 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 B O O O O O O O O

folksonomies
relational clustering
social metadata

collective knowledge
social information processing
taxonomies


Serendipitous learning : learning beyond the predefined label space Most traditional supervised learning methods are developed to learn a model from labeled examples and use this model to classify the unlabeled ones into the same label space predefined by the models . However , in many real world applications , the label spaces for both the labeled\/training and unlabeled\/testing examples can be different . To solve this problem , this paper proposes a novel notion of Serendipitous Learning ( SL ) , which is defined to address the learning scenarios in which the label space can be enlarged during the testing phase . In particular , a large margin approach is proposed to solve SL . The basic idea is to leverage the knowledge in the labeled examples to help identify novel\/unknown classes , and the large margin formulation is proposed to incorporate both the classification loss on the examples within the known categories , as well as the clustering loss on the examples in unknown categories . An efficient optimization algorithm based on CCCP and the bundle method is proposed to solve the optimization problem of the large margin formulation of SL . Moreover , an efficient online learning method is proposed to address the issue of large scale data in online learning scenario , which has been shown to have a guaranteed learning regret . An extensive set of experimental results on two synthetic datasets and two datasets from real world applications demonstrate the advantages of the proposed method over several other baseline algorithms . One limitation of the proposed method is that the number of unknown classes is given in advance . It may be possible to remove this constraint if we model it by using a non-parametric way . We also plan to do experiments on more real world applications in the future .

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


Making holistic schema matching robust : an ensemble approach The Web has been rapidly `` deepened '' by myriad searchable databases online , where data are hidden behind query interfaces . As an essential task toward integrating these massive `` deep Web '' sources , large scale schema matching ( i.e. , discovering semantic correspondences of attributes across many query interfaces ) has been actively studied recently . In particular , many works have emerged to address this problem by `` holistically '' matching many schemas at the same time and thus pursuing `` mining '' approaches in nature . However , while holistic schema matching has built its promise upon the large quantity of input schemas , it also suffers the robustness problem caused by noisy data quality . Such noises often inevitably arise in the automatic extraction of schema data , which is mandatory in large scale integration . For holistic matching to be viable , it is thus essential to make it robust against noisy schemas . To tackle this challenge , we propose a data-ensemble framework with sampling and voting techniques , which is inspired by bagging predictors . Specifically , our approach creates an ensemble of matchers , by randomizing input schema data into many independently downsampled trials , executing the same matcher on each trial and then aggregating their ranked results by taking majority voting . As a principled basis , we provide analytic justification of the effectiveness of this data-ensemble framework . Further , empirically , our experiments on real Web data show that the `` ensemblization '' indeed significantly boosts the matching accuracy under noisy schema input , and thus maintains the desired robustness of a holistic matcher .

O O B I 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 B I 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 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 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

bagging predictors
deep web
ensemble
schema matching

data integration
heterogeneous databases


IncSpan : incremental mining of sequential patterns in large database Many real life sequence databases grow incrementally . It is undesirable to mine sequential patterns from scratch each time when a small set of sequences grow , or when some new sequences are added into the database . Incremental algorithm should be developed for sequential pattern mining so that mining can be adapted to incremental database updates . However , it is nontrivial to mine sequential patterns incrementally , especially when the existing sequences grow incrementally because such growth may lead to the generation of many new patterns due to the interactions of the growing subsequences with the original ones . In this study , we develop an efficient algorithm , IncSpan , for incremental mining of sequential patterns , by exploring some interesting properties . Our performance study shows that IncSpan outperforms some previously proposed incremental algorithms as well as a non-incremental one with a wide margin .

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 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

buffering pattern
reverse pattern matching
shared projection


Generating semantic annotations for frequent patterns with context analysis As a fundamental data mining task , frequent pattern mining has widespread applications in many different domains . Research in frequent pattern mining has so far mostly focused on developing efficient algorithms to discover various kinds of frequent patterns , but little attention has been paid to the important nextstep - interpreting the discovered frequent patterns . Although some recent work has studied the compression and summarization of frequent patterns , the proposed techniques can only annotate a frequent pattern with non-semantical information ( e.g. support ) , which provides only limited help for a user to understand the patterns . In this paper , we propose the novel problem of generating semantic annotations for frequent patterns . The goal is to annotate a frequent pattern with in-depth , concise , and structured information that can better indicate the hidden meanings of the pattern . We propose a general approach to generate such anannotation for a frequent pattern by constructing its context model , selecting informative context indicators , and extracting representative transactions and semantically similar patterns . This general approach has potentially many applications such as generating a dictionary-like description for a pattern , finding synonym patterns , discovering semantic relations , and summarizing semantic classes of a set of frequent patterns . Experiments on different datasets show that our approach is effective in generating semantic pattern annotations .

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 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 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 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

pattern annotation
pattern context
pattern semantic analysis


Empirical bayes screening for multi-item associations This paper considers the framework of the so-called `` market basket problem '' , in which a database of transactions is mined for the occurrence of unusually frequent item sets . In our case , `` unusually frequent '' involves estimates of the frequency of each item set divided by a baseline frequency computed as if items occurred independently . The focus is on obtaining reliable estimates of this measure of interestingness for all item sets , even item sets with relatively low frequencies . For example , in a medical database of patient histories , unusual item sets including the item `` patient death '' ( or other serious adverse event ) might hopefully be flagged with as few as 5 or 10 occurrences of the item set , it being unacceptable to require that item sets occur in as many as 0.1 % of millions of patient reports before the data mining algorithm detects a signal . Similar considerations apply in fraud detection applications . Thus we abandon the requirement that interesting item sets must contain a relatively large fixed minimal support , and adopt a criterion based on the results of fitting an empirical Bayes model to the item set counts . The model allows us to define a 95 % Bayesian lower confidence limit for the `` interestingness '' measure of every item set , whereupon the item sets can be ranked according to their empirical Bayes confidence limits . For item sets of size J ) 2 , we also distinguish between multi-item associations that can be explained by the observed J ( J-1 ) \/ 2 pairwise associations , and item sets that are significantly more frequent than their pairwise associations would suggest . Such item sets can uncover complex or synergistic mechanisms generating multi-item associations . This methodology has been applied within the U.S. Food and Drug Administration ( FDA ) to databases of adverse drug reaction reports and within AT&T to customer international calling histories . We also present graphical techniques for exploring and understanding the modeling results .

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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 rules
empirical bayes methods
gamma-poisson model
knowledge discovery
shrinkage estimation
statistical models


Efficient search for association rules

association rule
information search and retrieval
learning
miscellaneous


Mining phenotypes and informative genes from gene expression data Mining microarray gene expression data is an important research topic in bioinformatics with broad applications . While most of the previous studies focus on clustering either genes or samples , it is interesting to ask whether we can partition the complete set of samples into exclusive groups ( called phenotypes ) and find a set of informative genes that can manifest the phenotype structure . In this paper , we propose a new problem of simultaneously mining phenotypes and informative genes from gene expression data . Some statistics-based metrics are proposed to measure the quality of the mining results . Two interesting algorithms are developed : the heuristic search and the mutual reinforcing adjustment method . We present an extensive performance study on both real-world data sets and synthetic data sets . The mining results from the two proposed methods are clearly better than those from the previous methods . They are ready for the real-world applications . Between the two methods , the mutual reinforcing adjustment method is in general more scalable , more effective and with better quality of the mining results .

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 B I O O O O B 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

bioinformatics
informative genes
phenotype


Mining unexpected rules by pushing user dynamics Unexpected rules are interesting because they are either previously unknown or deviate from what prior user knowledge would suggest . In this paper , we study three important issues that have been previously ignored in mining unexpected rules . First , the unexpectedness of a rule depends on how the user prefers to apply the prior knowledge to a given scenario , in addition to the knowledge itself . Second , the prior knowledge should be considered right from the start to focus the search on unexpected rules . Third , the unexpectedness of a rule depends on what other rules the user has seen so far . Thus , only rules that remain unexpected given what the user has seen should be considered interesting . We develop an approach that addresses all three problems above and evaluate it by means of experiments focusing on finding interesting rules .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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
subjective interestingness
unexpected rule


Customer targeting models using actively-selected web content We consider the problem of predicting the likelihood that a company will purchase a new product from a seller . The statistical models we have developed at IBM for this purpose rely on historical transaction data coupled with structured firmographic information like the company revenue , number of employees and so on . In this paper , we extend this methodology to include additional text-based features based on analysis of the content on each company 's website . Empirical results demonstrate that incorporating such web content can significantly improve customer targeting . Furthermore , we present methods to actively select only the web content that is likely to improve our models , while reducing the costs of acquisition and processing .

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 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

active feature-value acquisition
active learning
learning
text categorization
web mining


Social influence analysis in large-scale networks In large social networks , nodes ( users , entities ) are influenced by others for various reasons . For example , the colleagues have strong influence on one 's work , while the friends have strong influence on one 's daily life . How to differentiate the social influences from different angles ( topics ) ? How to quantify the strength of those social influences ? How to estimate the model on real large networks ? To address these fundamental questions , we propose Topical Affinity Propagation ( TAP ) to model the topic-level social influence on large networks . In particular , TAP can take results of any topic modeling and the existing network structure to perform topic-level influence propagation . With the help of the influence analysis , we present several important applications on real data sets such as 1 ) what are the representative nodes on a given topic ? 2 ) how to identify the social influences of neighboring nodes on a particular node ? To scale to real large networks , TAP is designed with efficient distributed learning algorithms that is implemented and tested under the Map-Reduce framework . We further present the common characteristics of distributed learning algorithms for Map-Reduce . Finally , we demonstrate the effectiveness and efficiency of TAP on real large data sets .

B I I 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

social influence analysis
social networks

database applications
information search and retrieval
large-scale network
topical analysis propagation


Pattern discovery in sequences under a Markov assumption In this paper we investigate the general problem of discovering recurrent patterns that are embedded in categorical sequences . An important real-world problem of this nature is motif discovery in DNA sequences . We investigate the fundamental aspects of this data mining problem that can make discovery `` easy '' or `` hard . '' We present a general framework for characterizing learning in this context by deriving the Bayes error rate for this problem under a Markov assumption . The Bayes error framework demonstrates why certain patterns are much harder to discover than others . It also explains the role of different parameters such as pattern length and pattern frequency in sequential discovery . We demonstrate how the Bayes error can be used to calibrate existing discovery algorithms , providing a lower bound on achievable performance . We discuss a number of fundamental issues that characterize sequential pattern discovery in this context , present a variety of empirical results to complement and verify the theoretical analysis , and apply our methodology to real-world motif-discovery problems in computational biology .

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


On the tradeoff between privacy and utility in data publishing In data publishing , anonymization techniques such as generalization and bucketization have been designed to provide privacy protection . In the meanwhile , they reduce the utility of the data . It is important to consider the tradeoff between privacy and utility . In a paper that appeared in KDD 2008 , Brickell and Shmatikov proposed an evaluation methodology by comparing privacy gain with utility gain resulted from anonymizing the data , and concluded that `` even modest privacy gains require almost complete destruction of the data-mining utility '' . This conclusion seems to undermine existing work on data anonymization . In this paper , we analyze the fundamental characteristics of privacy and utility , and show that it is inappropriate to directly compare privacy with utility . We then observe that the privacy-utility tradeoff in data publishing is similar to the risk-return tradeoff in financial investment , and propose an integrated framework for considering privacy-utility tradeoff , borrowing concepts from the Modern Portfolio Theory for financial investment . Finally , we evaluate our methodology on the Adult dataset from the UCI machine learning repository . Our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility .

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


Training linear SVMs in linear time Linear Support Vector Machines ( SVMs ) have become one of the most prominent machine learning techniques for high-dimensional sparse data commonly encountered in applications like text classification , word-sense disambiguation , and drug design . These applications involve a large number of examples n as well as a large number of features N , while each example has only s ( ( N non-zero features . This paper presents a Cutting Plane Algorithm for training linear SVMs that provably has training time 0 ( s , n ) for classification problems and o ( sn log ( n ) ) for ordinal regression problems . The algorithm is based on an alternative , but equivalent formulation of the SVM optimization problem . Empirically , the Cutting-Plane Algorithm is several orders of magnitude faster than decomposition methods like svm light for large datasets .

O O O O O O O B I I 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 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

learning
ordinal regression
support vector machines

large-scale problems
roc-area
training algorithms


Learning classifiers from only positive and unlabeled data The input to an algorithm that learns a binary classifier normally consists of two sets of examples , where one set consists of positive examples of the concept to be learned , and the other set consists of negative examples . However , it is often the case that the available training data are an incomplete set of positive examples , and a set of unlabeled examples , some of which are positive and some of which are negative . The problem solved in this paper is how to learn a standard binary classifier given a nontraditional training set of this nature . Under the assumption that the labeled examples are selected randomly from the positive examples , we show that a classifier trained on positive and unlabeled examples predicts probabilities that differ by only a constant factor from the true conditional probabilities of being positive . We show how to use this result in two different ways to learn a classifier from a nontraditional training set . We then apply these two new methods to solve a real-world problem : identifying protein records that should be included in an incomplete specialized molecular biology database . Our experiments in this domain show that models trained using the new methods perform better than the current state-of-the-art biased SVM method for learning from positive and unlabeled examples .

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

bioinformatics
supervised learning
text mining


Generative model-based clustering of directional data High dimensional directional data is becoming increasingly important in contemporary applications such as analysis of text and gene-expression data . A natural model for multi-variate directional data is provided by the von Mises-Fisher ( vMF ) distribution on the unit hypersphere that is analogous to the multi-variate Gaussian distribution in Rd. . In this paper , we propose modeling complex directional data as a mixture of vMF distributions . We derive and analyze two variants of the Expectation Maximization ( EM ) framework for estimating the parameters of this mixture . We also propose two clustering algorithms corresponding to these variants . An interesting aspect of our methodology is that the spherical kmeans algorithm ( kmeans with cosine similarity ) can be shown to be a special case of both our algorithms . Thus , modeling text data by vMF distributions lends theoretical validity to the use of cosine similarity which has been widely used by the information retrieval community . As part of experimental validation , we present results on modeling high-dimensional text and gene-expression data as a mixture of vMF distributions . The results indicate that our approach yields superior clusterings especially for difficult clustering tasks in high-dimensional spaces .

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

information search and retrieval
mixtures
von mises-fisher


Topics over time : a non-Markov continuous-time model of topical trends This paper presents an LDA-style topic model that captures not only the low-dimensional structure of data , but also how the structure changes over time . Unlike other recent work that relies on Markov assumptions or discretization of time , here each topic is associated with a continuous distribution over timestamps , and for each generated document , the mixture distribution over topics is influenced by both word co-occurrences and the document 's timestamp . Thus , the meaning of a particular topic can be relied upon as constant , but the topics ' occurrence and correlations change significantly over time . We present results on nine months of personal email , 17 years of NIPS research papers and over 200 years of presidential state-of-the-union addresses , showing improved topics , better timestamp prediction , and interpretable trends .

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

graphical models
learning
temporal analysis
topic modeling


Probabilistic topic models with biased propagation on heterogeneous information networks With the development of Web applications , textual documents are not only getting richer , but also ubiquitously interconnected with users and other objects in various ways , which brings about text-rich heterogeneous information networks . Topic models have been proposed and shown to be useful for document analysis , and the interactions among multi-typed objects play a key role at disclosing the rich semantics of the network . However , most of topic models only consider the textual information while ignore the network structures or can merely integrate with homogeneous networks . None of them can handle heterogeneous information network well . In this paper , we propose a novel topic model with biased propagation ( TMBP ) algorithm to directly incorporate heterogeneous information network with topic modeling in a unified way . The underlying intuition is that multi-typed objects should be treated differently along with their inherent textual information and the rich semantics of the heterogeneous information network . A simple and unbiased topic propagation across such a heterogeneous network does not make much sense . Consequently , we investigate and develop two biased propagation frameworks , the biased random walk framework and the biased regularization framework , for the TMBP algorithm from different perspectives , which can discover latent topics and identify clusters of multi-typed objects simultaneously . We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on several datasets . Experimental results demonstrate that the improvement in our proposed approach is consistent and promising .

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

biased propagation
heterogeneous information network
topic modeling


The `` DGX '' distribution for mining massive , skewed data Skewed distributions appear very often in practice . Unfortunately , the traditional Zipf distribution often fails to model them well . In this paper , we propose a new probability distribution , the Discrete Gaussian Exponential ( DGX ) , to achieve excellent fits in a wide variety of settings ; our new distribution includes the Zipf distribution as a special case . We present a statistically sound method for estimating the DGX parameters based on maximum likelihood estimation ( MLE ) . We applied DGX to a wide variety of real world data sets , such as sales data from a large retailer chain , us-age data from AT&T , and Internet clickstream data ; in all cases , DGX fits these distributions very well , with almost a 99 % correlation coefficient in quantile-quantile plots . Our algorithm also scales very well because it requires only a single pass over the data . Finally , we illustrate the power of DGX as a new tool for data mining tasks , such as outlier detection .

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

dgx
maximum likelihood estimation
outlier detection

frequency-count plot
lognormal distribution
rank-frequency plot
zipf's law


Adaptive duplicate detection using learnable string similarity measures The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes . Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates . In this paper , we present a framework for improving duplicate detection using trainable measures of textual similarity . We propose to employ learnable text distance functions for each database field , and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field 's domain . We present two learnable text similarity measures suitable for this task : an extended variant of learnable string edit distance , and a novel vector-space based measure that employs a Support Vector Machine ( SVM ) for training . Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques .

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 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

database applications
distance metric learning
learning
record linkage
svm applications
trained similarity measures


Probabilistic discovery of time series motifs Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series . In an earlier work , we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs . Two limitations of this work were the poor scalability of the motif discovery algorithm , and the inability to discover motifs in the presence of noise . Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences . Our algorithm is probabilistic in nature , but as we show empirically and theoretically , it can find time series motifs with very high probability even in the presence of noise or `` do n't care '' symbols . Not only is the algorithm fast , but it is an anytime algorithm , producing likely candidate motifs almost immediately , and gradually improving the quality of results over time .

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

database applications
randomized algorithms


Discovering frequent patterns in sensitive data Discovering frequent patterns from data is a popular exploratory technique in datamining . However , if the data are sensitive ( e.g. , patient health records , user behavior records ) releasing information about significant patterns or trends carries significant risk to privacy . This paper shows how one can accurately discover and release the most significant patterns along with their frequencies in a data set containing sensitive information , while providing rigorous guarantees of privacy for the individuals whose information is stored there . We present two efficient algorithms for discovering the k most frequent patterns in a data set of sensitive records . Our algorithms satisfy differential privacy , a recently introduced definition that provides meaningful privacy guarantees in the presence of arbitrary external information . Differentially private algorithms require a degree of uncertainty in their output to preserve privacy . Our algorithms handle this by returning ` noisy ' lists of patterns that are close to the actual list of k most frequent patterns in the data . We define a new notion of utility that quantifies the output accuracy of private top-k pattern mining algorithms . In typical data sets , our utility criterion implies low false positive and false negative rates in the reported lists . We prove that our methods meet the new utility criterion ; we also demonstrate the performance of our algorithms through extensive experiments on the transaction data sets from the FIMI repository . While the paper focuses on frequent pattern mining , the techniques developed here are relevant whenever the data mining output is a list of elements ordered according to an appropriately ` robust ' measure of interest .

O B 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 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 B 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 B 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 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

differential privacy
frequent patterns
privacy

exponential mechanism
frequent itemsets
general