Discovery of climate indices using clustering To analyze the effect of the oceans and atmosphere on land climate , Earth Scientists have developed climate indices , which are time series that summarize the behavior of selected regions of the Earth 's oceans and atmosphere . In the past , Earth scientists have used observation and , more recently , eigenvalue analysis techniques , such as principal components analysis ( PCA ) and singular value decomposition ( SVD ) , to discover climate indices . However , eigenvalue techniques are only useful for finding a few of the strongest signals . Furthermore , they impose a condition that all discovered signals must be orthogonal to each other , making it difficult to attach a physical interpretation to them . This paper presents an alternative clustering-based methodology for the discovery of climate indices that overcomes these limitiations and is based on clusters that represent regions with relatively homogeneous behavior . The centroids of these clusters are time series that summarize the behavior of the ocean or atmosphere in those regions . Some of these centroids correspond to known climate indices and provide a validation of our methodology ; other centroids are variants of known indices that may provide better predictive power for some land areas ; and still other indices may represent potentially new Earth science phenomena . Finally , we show that cluster based indices generally outperform SVD derived indices , both in terms of area weighted correlation and direct correlation with the known indices .

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 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 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 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

clustering
singular value decomposition
time series

earth science data
mining scientific data


Semi-supervised learning with data calibration for long-term time series forecasting Many time series prediction methods have focused on single step or short term prediction problems due to the inherent difficulty in controlling the propagation of errors from one prediction step to the next step . Yet , there is a broad range of applications such as climate impact assessments and urban growth planning that require long term forecasting capabilities for strategic decision making . Training an accurate model that produces reliable long term predictions would require an extensive amount of historical data , which are either unavailable or expensive to acquire . For some of these domains , there are alternative ways to generate potential scenarios for the future using computer-driven simulation models , such as global climate and traffic demand models . However , the data generated by these models are currently utilized in a supervised learning setting , where a predictive model trained on past observations is used to estimate the future values . In this paper , we present a semi-supervised learning framework for long-term time series forecasting based on Hidden Markov Model Regression . A covariance alignment method is also developed to deal with the issue of inconsistencies between historical and model simulation data . We evaluated our approach on data sets from a variety of domains , including climate modeling . Our experimental results demonstrate the efficacy of the approach compared to other supervised learning methods for long-term time series forecasting .

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


Pattern lattice traversal by selective jumps Regardless of the frequent patterns to discover , either the full frequent patterns or the condensed ones , either closed or maximal , the strategy always includes the traversal of the lattice of candidate patterns . We study the existing depth versus breadth traversal approaches for generating candidate patterns and propose in this paper a new traversal approach that jumps in the search space among only promising nodes . Our leaping approach avoids nodes that would not participate in the answer set and reduce drastically the number of candidate patterns . We use this approach to efficiently pinpoint maximal patterns at the border of the frequent patterns in the lattice and collect enough information in the process to generate all subsequent patterns .

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Very sparse random projections There has been considerable interest in random projections , an approximate algorithm for estimating distances between pairs of points in a high-dimensional vector space . Let A in Rn x D be our n points in D dimensions . The method multiplies A by a random matrix R in RD x k , reducing the D dimensions down to just k for speeding up the computation . R typically consists of entries of standard normal N ( 0,1 ) . It is well known that random projections preserve pairwise distances ( in the expectation ) . Achlioptas proposed sparse random projections by replacing the N ( 0,1 ) entries in R with entries in -1,0,1 with probabilities 1\/6 , 2\/3 , 1\/6 , achieving a threefold speedup in processing time . We recommend using R of entries in -1,0,1 with probabilities 1\/2 √ D , 1-1 √ D , 1\/2 √ D for achieving a significant √ D-fold speedup , with little loss in accuracy .

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


YALE : rapid prototyping for complex data mining tasks KDD is a complex and demanding task . While a large number of methods has been established for numerous problems , many challenges remain to be solved . New tasks emerge requiring the development of new methods or processing schemes . Like in software development , the development of such solutions demands for careful analysis , specification , implementation , and testing . Rapid prototyping is an approach which allows crucial design decisions as early as possible . A rapid prototyping system should support maximal re-use and innovative combinations of existing methods , as well as simple and quick integration of new ones . This paper describes Yale , a free open-source environment forKDD and machine learning . Yale provides a rich variety of methods whichallows rapid prototyping for new applications and makes costlyre-implementations unnecessary . Additionally , Yale offers extensive functionality for process evaluation and optimization which is a crucial property for any KDD rapid prototyping tool . Following the paradigm of visual programming eases the design of processing schemes . While the graphical user interface supports interactive design , the underlying XML representation enables automated applications after the prototyping phase . After a discussion of the key concepts of Yale , we illustrate the advantages of rapid prototyping for KDD on case studies ranging from data pre-processing to result visualization . These case studies cover tasks like feature engineering , text mining , data stream mining and tracking drifting concepts , ensemble methods and distributed data mining . This variety of applications is also reflected in a broad user base , we counted more than 40,000 downloads during the last twelve months .

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

data stream mining
distributed data mining
rapid prototyping
result visualization

audio and text mining
data pre-processing
design methodology
feature construction
kdd system
multimedia mining


Bayesian analysis of massive datasets via particle filters Markov Chain Monte Carlo ( MCMC ) techniques revolutionized statistical practice in the 1990s by providing an essential toolkit for making the rigor and flexibility of Bayesian analysis computationally practical . At the same time the increasing prevalence of massive datasets and the expansion of the field of data mining has created the need to produce statistically sound methods that scale to these large problems . Except for the most trivial examples , current MCMC methods require a complete scan of the dataset for each iteration eliminating their candidacy as feasible data mining techniques . In this article we present a method for making Bayesian analysis of massive datasets computationally feasible . The algorithm simulates from a posterior distribution that conditions on a smaller , more manageable portion of the dataset . The remainder of the dataset may be incorporated by reweighting the initial draws using importance sampling . Computation of the importance weights requires a single scan of the remaining observations . While importance sampling increases efficiency in data access , it comes at the expense of estimation efficiency . A simple modification , based on the `` rejuvenation '' step used in particle filters for dynamic systems models , sidesteps the loss of efficiency with only a slight increase in the number of data accesses . To show proof-of-concept , we demonstrate the method on a mixture of transition models that has been used to model web traffic and robotics . For this example we show that estimation efficiency is not affected while offering a 95 % reduction in data accesses .

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


CLOPE : a fast and effective clustering algorithm for transactional data This paper studies the problem of categorical data clustering , especially for transactional data characterized by high dimensionality and large volume . Starting from a heuristic method of increasing the height-to-width ratio of the cluster histogram , we develop a novel algorithm -- CLOPE , which is very fast and scalable , while being quite effective . We demonstrate the performance of our algorithm on two real world datasets , and compare CLOPE with the state-of-art algorithms .

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Partitioned logistic regression for spam filtering Naive Bayes and logistic regression perform well in different regimes . While the former is a very simple generative model which is efficient to train and performs well empirically in many applications , the latter is a discriminative model which often achieves better accuracy and can be shown to outperform naive Bayes asymptotically . In this paper , we propose a novel hybrid model , partitioned logistic regression , which has several advantages over both naive Bayes and logistic regression . This model separates the original feature space into several disjoint feature groups . Individual models on these groups of features are learned using logistic regression and their predictions are combined using the naive Bayes principle to produce a robust final estimation . We show that our model is better both theoretically and empirically . In addition , when applying it in a practical application , email spam filtering , it improves the normalized AUC score at 10 % false-positive rate by 28.8 % and 23.6 % compared to naive Bayes and logistic regression , when using the exact same training examples .

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

email spam filtering
logistic regression
naive bayes


A classification-based methodology for planning audit strategies in fraud detection

classification
decision trees
integration of querying and mining
knowledge discovery in databases
logic-based database languages


Classification of software behaviors for failure detection : a discriminative pattern mining approach Software is a ubiquitous component of our daily life . We often depend on the correct working of software systems . Due to the difficulty and complexity of software systems , bugs and anomalies are prevalent . Bugs have caused billions of dollars loss , in addition to privacy and security threats . In this work , we address software reliability issues by proposing a novel method to classify software behaviors based on past history or runs . With the technique , it is possible to generalize past known errors and mistakes to capture failures and anomalies . Our technique first mines a set of discriminative features capturing repetitive series of events from program execution traces . It then performs feature selection to select the best features for classification . These features are then used to train a classifier to detect failures . Experiments and case studies on traces of several benchmark software systems and a real-life concurrency bug from MySQL server show the utility of the technique in capturing failures and anomalies . On average , our pattern-based classification technique outperforms the baseline approach by 24.68 % in accuracy .

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 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

closed unique patterns
iterative patterns
pattern-based classification
sequential database


Query chains : learning to rank from implicit feedback This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results . We observe that users searching the web often perform a sequence , or chain , of queries with a similar information need . Using query chains , we generate new types of preference judgments from search engine logs , thus taking advantage of user intelligence in reformulating queries . To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments . We also implemented a real-world search engine to test our approach , using a modified ranking SVM to learn an improved ranking function from preference data . Our results demonstrate significant improvements in the ranking given by the search engine . The learned rankings outperform both a static ranking function , as well as one trained without considering query chains .

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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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
machine learning
search engines
support vector machines


Mining discrete patterns via binary matrix factorization Mining discrete patterns in binary data is important for subsampling , compression , and clustering . We consider rank-one binary matrix approximations that identify the dominant patterns of the data , while preserving its discrete property . A best approximation on such data has a minimum set of inconsistent entries , i.e. , mismatches between the given binary data and the approximate matrix . Due to the hardness of the problem , previous accounts of such problems employ heuristics and the resulting approximation may be far away from the optimal one . In this paper , we show that the rank-one binary matrix approximation can be reformulated as a 0-1 integer linear program ( ILP ) . However , the ILP formulation is computationally expensive even for small-size matrices . We propose a linear program ( LP ) relaxation , which is shown to achieve a guaranteed approximation error bound . We further extend the proposed formulations using the regularization technique , which is commonly employed to address overfitting . The LP formulation is restricted to medium-size matrices , due to the large number of variables involved for large matrices . Interestingly , we show that the proposed approximate formulation can be transformed into an instance of the minimum s-t cut problem , which can be solved efficiently by finding maximum flows . Our empirical study shows the efficiency of the proposed algorithm based on the maximum flow . Results also confirm the established theoretical bounds .

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 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 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 B I O O O O O O O O O

binary matrix factorization
integer linear program
maximum flow
regularization


Automatic identification of quasi-experimental designs for discovering causal knowledge Researchers in the social and behavioral sciences routinely rely on quasi-experimental designs to discover knowledge from large data-bases . Quasi-experimental designs ( QEDs ) exploit fortuitous circumstances in non-experimental data to identify situations ( sometimes called `` natural experiments '' ) that provide the equivalent of experimental control and randomization . QEDs allow researchers in domains as diverse as sociology , medicine , and marketing to draw reliable inferences about causal dependencies from non-experimental data . Unfortunately , identifying and exploiting QEDs has remained a painstaking manual activity , requiring researchers to scour available databases and apply substantial knowledge of statistics . However , recent advances in the expressiveness of databases , and increases in their size and complexity , provide the necessary conditions to automatically identify QEDs . In this paper , we describe the first system to discover knowledge by applying quasi-experimental designs that were identified automatically . We demonstrate that QEDs can be identified in a traditional database schema and that such identification requires only a small number of extensions to that schema , knowledge about quasi-experimental design encoded in first-order logic , and a theorem-proving engine . We describe several key innovations necessary to enable this system , including methods for automatically constructing appropriate experimental units and for creating aggregate variables on those units . We show that applying the resulting designs can identify important causal dependencies in real domains , and we provide examples from academic publishing , movie making and marketing , and peer-production systems . Finally , we discuss the integration of QEDs with other approaches to causal discovery , including joint modeling and directed experimentation .

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


Graph-based anomaly detection Anomaly detection is an area that has received much attention in recent years . It has a wide variety of applications , including fraud detection and network intrusion detection . A good deal of research has been performed in this area , often using strings or attribute-value data as the medium from which anomalies are to be extracted . Little work , however , has focused on anomaly detection in graph-based data . In this paper , we introduce two techniques for graph-based anomaly detection . In addition , we introduce a new method for calculating the regularity of a graph , with applications to anomaly detection . We hypothesize that these methods will prove useful both for finding anomalies , and for determining the likelihood of successful anomaly detection within graph-based data . We provide experimental results using both real-world network intrusion data and artificially-created data .

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


Relational learning via collective matrix factorization Relational learning is concerned with predicting unknown values of a relation , given a database of entities and observed relations among entities . An example of relational learning is movie rating prediction , where entities could include users , movies , genres , and actors . Relations encode users ' ratings of movies , movies ' genres , and actors ' roles in movies . A common prediction technique given one pairwise relation , for example a #users x #movies ratings matrix , is low-rank matrix factorization . In domains with multiple relations , represented as multiple matrices , we may improve predictive accuracy by exploiting information from one relation while predicting another . To this end , we propose a collective matrix factorization model : we simultaneously factor several matrices , sharing parameters among factors when an entity participates in multiple relations . Each relation can have a different value type and error distribution ; so , we allow nonlinear relationships between the parameters and outputs , using Bregman divergences to measure error . We extend standard alternating projection algorithms to our model , and derive an efficient Newton update for the projection . Furthermore , we propose stochastic optimization methods to deal with large , sparse matrices . Our model generalizes several existing matrix factorization methods , and therefore yields new large-scale optimization algorithms for these problems . Our model can handle any pairwise relational schema and a wide variety of error models . We demonstrate its efficiency , as well as the benefit of sharing parameters among relations .

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

matrix factorization
relational learning


Spatially regularized logistic regression for disease mapping on large moving populations Spatial analysis of disease risk , or disease mapping , typically relies on information about the residence and health status of individuals from population under study . However , residence information has its limitations because people are exposed to numerous disease risks as they spend time outside of their residences . Thanks to the wide-spread use of mobile phones and GPS-enabled devices , it is becoming possible to obtain a detailed record about the movement of human populations . Availability of movement information opens up an opportunity to improve the accuracy of disease mapping . Starting with an assumption that an individual 's disease risk is a weighted average of risks at the locations which were visited , we show that disease mapping can be accomplished by spatially regularized logistic regression . Due to the inherent sparsity of movement data , the proposed approach can be applied to large populations and over large spatial grids . In our experiments , we were able to map disease for a simulated population with 1.6 million people and a spatial grid with 65 thousand locations in several minutes . The results indicate that movement information can improve the accuracy of disease mapping as compared to residential data only . We also studied a privacy-preserving scenario in which only the aggregate statistics are available about the movement of the overall population , while detailed movement information is available only for individuals with disease . The results indicate that the accuracy of disease mapping remains satisfactory when learning from movement data sanitized in this way .

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

movement trajectories
privacy
regularization
spatial epidemiology
spatial-temporal data mining


Systematic data selection to mine concept-drifting data streams One major problem of existing methods to mine data streams is that it makes ad hoc choices to combine most recent data with some amount of old data to search the new hypothesis . The assumption is that the additional old data always helps produce a more accurate hypothesis than using the most recent data only . We first criticize this notion and point out that using old data blindly is not better than `` gambling '' ; in other words , it helps increase the accuracy only if we are `` lucky . '' We discuss and analyze the situations where old data will help and what kind of old data will help . The practical problem on choosing the right example from old data is due to the formidable cost to compare different possibilities and models . This problem will go away if we have an algorithm that is extremely efficient to compare all sensible choices with little extra cost . Based on this observation , we propose a simple , efficient and accurate cross-validation decision tree ensemble method .

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


Targeting the right students using data mining

data mining application in education
scoring
target selection


Co-evolution of social and affiliation networks In our work , we address the problem of modeling social network generation which explains both link and group formation . Recent studies on social network evolution propose generative models which capture the statistical properties of real-world networks related only to node-to-node link formation . We propose a novel model which captures the co-evolution of social and affiliation networks . We provide surprising insights into group formation based on observations in several real-world networks , showing that users often join groups for reasons other than their friends . Our experiments show that the model is able to capture both the newly observed and previously studied network properties . This work is the first to propose a generative model which captures the statistical properties of these complex networks . The proposed model facilitates controlled experiments which study the effect of actors ' behavior on the evolution of affiliation networks , and it allows the generation of realistic synthetic datasets .

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


Applying data mining in investigating money laundering crimes In this paper , we study the problem of applying data mining to facilitate the investigation of money laundering crimes ( MLCs ) . We have identified a new paradigm of problems -- that of automatic community generation based on uni-party data , the data in which there is no direct or explicit link information available . Consequently , we have proposed a new methodology for Link Discovery based on Correlation Analysis ( LDCA ) . We have used MLC group model generation as an exemplary application of this problem paradigm , and have focused on this application to develop a specific method of automatic MLC group model generation based on timeline analysis using the LDCA methodology , called CORAL . A prototype of CORAL method has been implemented , and preliminary testing and evaluations based on a real MLC case data are reported . The contributions of this work are : ( 1 ) identification of the uni-party data community generation problem paradigm , ( 2 ) proposal of a new methodology LDCA to solve for problems in this paradigm , ( 3 ) formulation of the MLC group model generation problem as an example of this paradigm , ( 4 ) application of the LDCA methodology in developing a specific solution ( CORAL ) to the MLC group model generation problem , and ( 5 ) development , evaluation , and testing of the CORAL prototype in a real MLC case data .

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

link discovery based on correlation analysis

bi-party data
clustering
histogram
mlc group models
uni-party data


A visual-analytic toolkit for dynamic interaction graphs In this article we describe a visual-analytic tool for the interrogation of evolving interaction network data such as those found in social , bibliometric , WWW and biological applications . The tool we have developed incorporates common visualization paradigms such as zooming , coarsening and filtering while naturally integrating information extracted by a previously described event-driven framework for characterizing the evolution of such networks . The visual front-end provides features that are specifically useful in the analysis of interaction networks , capturing the dynamic nature of both individual entities as well as interactions among them . The tool provides the user with the option of selecting multiple views , designed to capture different aspects of the evolving graph from the perspective of a node , a community or a subset of nodes of interest . Standard visual templates and cues are used to highlight critical changes that have occurred during the evolution of the network . A key challenge we address in this work is that of scalability - handling large graphs both in terms of the efficiency of the back-end , and in terms of the efficiency of the visual layout and rendering . Two case studies based on bibliometric and Wikipedia data are presented to demonstrate the utility of the toolkit for visual knowledge discovery .

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

dynamic interaction networks
graph visualization
visual analytics


Model-based overlapping clustering While the vast majority of clustering algorithms are partitional , many real world datasets have inherently overlapping clusters . Several approaches to finding overlapping clusters have come from work on analysis of biological datasets . In this paper , we interpret an overlapping clustering model proposed by Segal et al. ( 23 ) as a generalization of Gaussian mixture models , and we extend it to an overlapping clustering model based on mixtures of any regular exponential family distribution and the corresponding Bregman divergence . We provide the necessary algorithm modifications for this extension , and present results on synthetic data as well as subsets of 20-Newsgroups and EachMovie datasets .

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 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

bregman divergences
exponential model
graphical model
high-dimensional clustering
learning


Generalized additive neural networks

additive models
financial
partial residuals
predictive modeling


Universal multi-dimensional scaling In this paper , we propose a unified algorithmic framework for solving many known variants of MDS . Our algorithm is a simple iterative scheme with guaranteed convergence , and is modular ; by changing the internals of a single subroutine in the algorithm , we can switch cost functions and target spaces easily . In addition to the formal guarantees of convergence , our algorithms are accurate ; in most cases , they converge to better quality solutions than existing methods in comparable time . Moreover , they have a small memory footprint and scale effectively for large data sets . We expect that this framework will be useful for a number of MDS variants that have not yet been studied . Our framework extends to embedding high-dimensional points lying on a sphere to points on a lower dimensional sphere , preserving geodesic distances . As a complement to this result , we also extend the Johnson-Lindenstrauss Lemma to this spherical setting , by showing that projecting to a random O ( ( 1 \/ µ2 ) log n ) - dimensional sphere causes only an eps-distortion in the geodesic distances .

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

dimensionality reduction
multi-dimensional scaling


Co-clustering based classification for out-of-domain documents In many real world applications , labeled data are in short supply . It often happens that obtaining labeled data in a new domain is expensive and time consuming , while there may be plenty of labeled data from a related but different domain . Traditional machine learning is not able to cope well with learning across different domains . In this paper , we address this problem for a text-mining task , where the labeled data are under one distribution in one domain known as in-domain data , while the unlabeled data are under a related but different domain known as out-of-domain data . Our general goal is to learn from the in-domain and apply the learned knowledge to out-of-domain . We propose a co-clustering based classification ( CoCC ) algorithm to tackle this problem . Co-clustering is used as a bridge to propagate the class structure and knowledge from the in-domain to the out-of-domain . We present theoretical and empirical analysis to show that our algorithm is able to produce high quality classification results , even when the distributions between the two data are different . The experimental results show that our algorithm greatly improves the classification performance over the traditional learning algorithms .

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 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 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

co-clustering
kullback-leibler divergence
out-of-domain


Using graph-based metrics with empirical risk minimization to speed up active learning on networked data Active and semi-supervised learning are important techniques when labeled data are scarce . Recently a method was suggested for combining active learning with a semi-supervised learning algorithm that uses Gaussian fields and harmonic functions . This classifier is relational in nature : it relies on having the data presented as a partially labeled graph ( also known as a within-network learning problem ) . This work showed yet again that empirical risk minimization ( ERM ) was the best method to find the next instance to label and provided an efficient way to compute ERM with the semi-supervised classifier . The computational problem with ERM is that it relies on computing the risk for all possible instances . If we could limit the candidates that should be investigated , then we can speed up active learning considerably . In the case where the data is graphical in nature , we can leverage the graph structure to rapidly identify instances that are likely to be good candidates for labeling . This paper describes a novel hybrid approach of using of community finding and social network analytic centrality measures to identify good candidates for labeling and then using ERM to find the best instance in this candidate set . We show on real-world data that we can limit the ERM computations to a fraction of instances with comparable performance .

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

active learning
community finding
empirical risk minimization

betweenness centrality
closeness centrality
clustering
semi-supervised learning
social network analysis
statistical relational learning
within-network learning


Critical event prediction for proactive management in large-scale computer clusters As the complexity of distributed computing systems increases , systems management tasks require significantly higher levels of automation ; examples include diagnosis and prediction based on real-time streams of computer events , setting alarms , and performing continuous monitoring . The core of autonomic computing , a recently proposed initiative towards next-generation IT-systems capable of ` self-healing ' , is the ability to analyze data in real-time and to predict potential problems . The goal is to avoid catastrophic failures through prompt execution of remedial actions . This paper describes an attempt to build a proactive prediction and control system for large clusters . We collected event logs containing various system reliability , availability and serviceability ( RAS ) events , and system activity reports ( SARs ) from a 350-node cluster system for a period of one year . The ` raw ' system health measurements contain a great deal of redundant event data , which is either repetitive in nature or misaligned with respect to time . We applied a filtering technique and modeled the data into a set of primary and derived variables . These variables used probabilistic networks for establishing event correlations through prediction algorithms . We also evaluated the role of time-series methods , rule-based classification algorithms and Bayesian network models in event prediction . Based on historical data , our results suggest that it is feasible to predict system performance parameters ( SARs ) with a high degree of accuracy using time-series models . Rule-based classification techniques can be used to extract machine-event signatures to predict critical events with up to 70 % accuracy .

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

database applications
large-scale clusters
learning
system event log


When do data mining results violate privacy ? Privacy-preserving data mining has concentrated on obtaining valid results when the input data is private . An extreme example is Secure Multiparty Computation-based methods , where only the results are revealed . However , this still leaves a potential privacy breach : Do the results themselves violate privacy ? This paper explores this issue , developing a framework under which this question can be addressed . Metrics are proposed , along with analysis that those metrics are consistent in the face of apparent problems .

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 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


Generating succinct titles for web URLs How can a search engine automatically provide the best and most appropriate title for a result URL ( link-title ) so that users will be persuaded to click on the URL ? We consider the problem of automatically generating link-titles for URLs and propose a general statistical framework for solving this problem . The framework is based on using information from a diverse collection of sources , each of which can be thought of as contributing one or more candidate link-titles for the URL . It can also incorporate the context in which the link-title will be used , along with constraints on its length . Our framework is applicable to several scenarios : obtaining succinct titles for displaying quicklinks , obtaining titles for URLs that lack a good title , constructing succinct sitemaps , etc. . Extensive experiments show that our method is very effective , producing results that are at least 20 % better than non-trivial baselines .

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

information search and retrieval
web page title generation


Finding a team of experts in social networks Given a task T , a pool of individuals X with different skills , and a social network G that captures the compatibility among these individuals , we study the problem of finding X , a subset of X , to perform the task . We call this the TEAM FORMATION problem . We require that members of X ' not only meet the skill requirements of the task , but can also work effectively together as a team . We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X ' . We study two variants of the problem for two different communication-cost functions , and show that both variants are NP-hard . We explore their connections with existing combinatorial problems and give novel algorithms for their solution . To the best of our knowledge , this is the first work to consider the TEAM FORMATION problem in the presence of a social network of individuals . Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results .

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


XRules : an effective structural classifier for XML data XML documents have recently become ubiquitous because of their varied applicability in a number of applications . Classification is an important problem in the data mining domain , but current classification methods for XML documents use IR-based methods in which each document is treated as a bag of words . Such techniques ignore a significant amount of information hidden inside the documents . In this paper we discuss the problem of rule based classification of XML data by using frequent discriminatory substructures within XML documents . Such a technique is more capable of finding the classification characteristics of documents . In addition , the technique can also be extended to cost sensitive classification . We show the effectiveness of the method with respect to other classifiers . We note that the methodology discussed in this paper is applicable to any kind of semi-structured data .

O O 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 O O O O O O O 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 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

database applications
tree mining
xml/semi-structured data


A hybrid unsupervised approach for document clustering We propose a hybrid , unsupervised document clustering approach that combines a hierarchical clustering algorithm with Expectation Maximization . We developed several heuristics to automatically select a subset of the clusters generated by the first algorithm as the initial points of the second one . Furthermore , our initialization algorithm generates not only an initial model for the iterative refinement algorithm but also an estimate of the model dimension , thus eliminating another important element of human supervision . We have evaluated the proposed system on five real-world document collections . The results show that our approach generates clustering solutions of higher quality than both its individual components .

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

em initialization
information search and retrieval
unsupervised clustering


Mining time-changing data streams Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution . Unfortunately , most of the large databases available for mining today violate this assumption . They were gathered over months or years , and the underlying processes generating them changed during this time , sometimes radically . Although a number of algorithms have been proposed for learning time-changing concepts , they generally do not scale well to very large databases . In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams , based on the ultra-fast VFDT decision tree learner . This algorithm , called CVFDT , stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable , and replacing the old with the new when the new becomes more accurate . CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives , but with O ( 1 ) complexity per example , as opposed to O ( w ) , where w is the size of the window . Experiments on a set of large time-changing data streams demonstrate the utility of this approach .

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

concept drift
hoeffding bounds
incremental learning
subsampling


Clustering seasonality patterns in the presence of errors Clustering is a very well studied problem that attempts to group similar data points . Most traditional clustering algorithms assume that the data is provided without measurement error . Often , however , real world data sets have such errors and one can obtain estimates of these errors . We present a clustering method that incorporates information contained in these error estimates . We present a new distance function that is based on the distribution of errors in data . Using a Gaussian model for errors , the distance function follows a Chi-Square distribution and is easy to compute . This distance function is used in hierarchical clustering to discover meaningful clusters . The distance function is scale-invariant so that clustering results are independent of units of measuring data . In the special case when the error distribution is the same for each attribute of data points , the rank order of pair-wise distances is the same for our distance function and the Euclidean distance function . The clustering method is applied to the seasonality estimation problem and experimental results are presented for the retail industry data as well as for simulated data , where it outperforms classical clustering methods .

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

forecasting
gaussian distribution
product life cycle
time-series


Grafting-light : fast , incremental feature selection and structure learning of Markov random fields Feature selection is an important task in order to achieve better generalizability in high dimensional learning , and structure learning of Markov random fields ( MRFs ) can automatically discover the inherent structures underlying complex data . Both problems can be cast as solving an l1-norm regularized parameter estimation problem . The existing Grafting method can avoid doing inference on dense graphs in structure learning by incrementally selecting new features . However , Grafting performs a greedy step to optimize over free parameters once new features are included . This greedy strategy results in low efficiency when parameter learning is itself non-trivial , such as in MRFs , in which parameter learning depends on an expensive subroutine to calculate gradients . The complexity of calculating gradients in MRFs is typically exponential to the size of maximal cliques . In this paper , we present a fast algorithm called Grafting-Light to solve the l1-norm regularized maximum likelihood estimation of MRFs for efficient feature selection and structure learning . Grafting-Light iteratively performs one-step of orthant-wise gradient descent over free parameters and selects new features . This lazy strategy is guaranteed to converge to the global optimum and can effectively select significant features . On both synthetic and real data sets , we show that Grafting-Light is much more efficient than Grafting for both feature selection and structure learning , and performs comparably with the optimal batch method that directly optimizes over all the features for feature selection but is much more efficient and accurate for structure learning of MRFs .

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

feature selection
markov random fields
structure learning


Identifying early buyers from purchase data Market research has shown that consumers exhibit a variety of different purchasing behaviors ; specifically , some tend to purchase products earlier than other consumers . Identifying such early buyers can help personalize marketing strategies , potentially improving their effectiveness . In this paper , we present a non-parametric approach to the problem of identifying early buyers from purchase data . Our formulation takes as inputs the detailed purchase information of each consumer , with which we construct a weighted directed graph whose nodes correspond to consumers and whose edges correspond to purchases consumers have in common ; the edge weights indicate how frequently consumers purchase products earlier than other consumers . Identifying early buyers corresponds to the problem of finding a subset of nodes in the graph with maximum difference between the weights of the outgoing and incoming edges . This problem is a variation of the maximum cut problem in a directed graph . We provide an approximation algorithm based on semidefinite programming ( SDP ) relaxations pioneered by Goemans and Williamson , and analyze its performance . We apply the algorithm to real purchase data from Amazon.com , providing new insights into consumer behaviors .

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


Learning from multi-topic web documents for contextual advertisement Contextual advertising on web pages has become very popular recently and it poses its own set of unique text mining challenges . Often advertisers wish to either target ( or avoid ) some specific content on web pages which may appear only in a small part of the page . Learning for these targeting tasks is difficult since most training pages are multi-topic and need expensive human labeling at the sub-document level for accurate training . In this paper we investigate ways to learn for sub-document classification when only page level labels are available - these labels only indicate if the relevant content exists in the given page or not . We propose the application of multiple-instance learning to this task to improve the effectiveness of traditional methods . We apply sub-document classification to two different problems in contextual advertising . One is `` sensitive content detection '' where the advertiser wants to avoid content relating to war , violence , pornography , etc. even if they occur only in a small part of a page . The second problem involves opinion mining from review sites - the advertiser wants to detect and avoid negative opinion about their product when positive , negative and neutral sentiments co-exist on a page . In both these scenarios we present experimental results to show that our proposed system is able to get good block level labeling for free and improve the performance of traditional learning methods .

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

contextual advertising
opinion mining
sensitive content detection


Dynamic syslog mining for network failure monitoring Syslog monitoring technologies have recently received vast attentions in the areas of network management and network monitoring . They are used to address a wide range of important issues including network failure symptom detection and event correlation discovery . Syslogs are intrinsically dynamic in the sense that they form a time series and that their behavior may change over time . This paper proposes a new methodology of dynamic syslog mining in order to detect failure symptoms with higher confidence and to discover sequential alarm patterns among computer devices . The key ideas of dynamic syslog mining are 1 ) to represent syslog behavior using a mixture of Hidden Markov Models , 2 ) to adaptively learn the model using an on-line discounting learning algorithm in combination with dynamic selection of the optimal number of mixture components , and 3 ) to give anomaly scores using universal test statistics with a dynamically optimized threshold . Using real syslog data we demonstrate the validity of our methodology in the scenarios of failure symptom detection , emerging pattern identification , and correlation discovery .

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

artificial intelligence
correlation analysis
failure detection
model selection
probabilistic modeling


Application of neural networks to biological data mining : a case study in protein sequence classification

bioinformatics
feature extraction from protein data
machine learning
sequence alignment


Factorization meets the neighborhood : a multifaceted collaborative filtering model Recommender systems provide users with personalized suggestions for products or services . These systems often rely on Collaborating Filtering ( CF ) , where past transactions are analyzed in order to establish connections between users and products . The two more successful approaches to CF are latent factor models , which directly profile both users and products , and neighborhood models , which analyze similarities between products or users . In this work we introduce some innovations to both approaches . The factor and neighborhood models can now be smoothly merged , thereby building a more accurate combined model . Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users . The methods are tested on the Netflix data . Results are better than those previously published on that dataset . In addition , we suggest a new evaluation metric , which highlights the differences among methods , based on their performance at a top-K recommendation task .

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

collaborative filtering
recommender systems


Characterising the difference Characterising the differences between two databases is an often occurring problem in Data Mining . Detection of change over time is a prime example , comparing databases from two branches is another one . The key problem is to discover the patterns that describe the difference . Emerging patterns provide only a partial answer to this question . In previous work , we showed that the data distribution can be captured in a pattern-based model using compression ( 12 ) . Here , we extend this approach to define a generic dissimilarity measure on databases . Moreover , we show that this approach can identify those patterns that characterise the differences between two distributions . Experimental results show that our method provides a well-founded way to independently measure database dissimilarity that allows for thorough inspection of the actual differences . This illustrates the use of our approach in real world data mining .

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


The minimum consistent subset cover problem and its applications in data mining In this paper , we introduce and study the Minimum Consistent Subset Cover ( MCSC ) problem . Given a finite ground set X and a constraint t , find the minimum number of consistent subsets that cover X , where a subset of X is consistent if it satisfies t. The MCSC problem generalizes the traditional set covering problem and has Minimum Clique Partition , a dual problem of graph coloring , as an instance . Many practical data mining problems in the areas of rule learning , clustering , and frequent pattern mining can be formulated as MCSC instances . In particular , we discuss the Minimum Rule Set problem that minimizes model complexity of decision rules as well as some converse k-clustering problems that minimize the number of clusters satisfying certain distance constraints . We also show how the MCSC problem can find applications in frequent pattern summarization . For any of these MCSC formulations , our proposed novel graph-based generic algorithm CAG can be directly applicable . CAG starts by constructing a maximal optimal partial solution , then performs an example-driven specific-to-general search on a dynamically maintained bipartite assignment graph to simultaneously learn a set of consistent subsets with small cardinality covering the ground set . Our experiments on benchmark datasets show that CAG achieves good results compared to existing popular heuristics .

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

minimum consistent subset cover
minimum rule set
pattern summarization


Scalable discovery of hidden emails from large folders The popularity of email has triggered researchers to look for ways to help users better organize the enormous amount of information stored in their email folders . One challenge that has not been studied extensively in text mining is the identification and reconstruction of hidden emails . A hidden email is an original email that has been quoted in at least one email in a folder , but does not present itself in the same folder . It may have been ( un ) intentionally deleted or may never have been received . The discovery and reconstruction of hidden emails is critical for many applications including email classification , summarization and forensics . This paper proposes a framework for reconstructing hidden emails using the embedded quotations found in messages further down the thread hierarchy . We evaluate the robustness and scalability of our framework by using the Enron public email corpus . Our experiments show that hidden emails exist widely in that corpus and also that our optimization techniques are effective in processing large email folders .

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


Fast best-effort pattern matching in large attributed graphs We focus on large graphs where nodes have attributes , such as a social network where the nodes are labeled with each person 's job title . In such a setting , we want to find subgraphs that match a user query pattern . For example , a `` star '' query would be , `` find a CEO who has strong interactions with a Manager , a Lawyer , and an Accountant , or another structure as close to that as possible '' . Similarly , a `` loop '' query could help spot a money laundering ring . Traditional SQL-based methods , as well as more recent graph indexing methods , will return no answer when an exact match does not exist . This is the first main feature of our method . It can find exact - , as well as near-matches , and it will present them to the user in our proposed `` goodness '' order . For example , our method tolerates indirect paths between , say , the `` CEO '' and the `` Accountant '' of the above sample query , when direct paths do n't exist . Its second feature is scalability . In general , if the query has nq nodes and the data graph has n nodes , the problem needs polynomial time complexity O ( n n q ) , which is prohibitive . Our G-Ray ( `` Graph X-Ray '' ) method finds high-quality subgraphs in time linear on the size of the data graph . Experimental results on the DLBP author-publication graph ( with 356K nodes and 1.9 M edges ) illustrate both the effectiveness and scalability of our approach . The results agree with our intuition , and the speed is excellent . It takes 4 seconds on average fora 4-node query on the DBLP graph .

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

attributed graph
miscellaneous
pattern match
random walk


Optimizing time series discretization for knowledge discovery Knowledge Discovery in time series usually requires symbolic time series . Many discretization methods that convert numeric time series to symbolic time series ignore the temporal order of values . This often leads to symbols that do not correspond to states of the process generating the time series and can not be interpreted meaningfully . We propose a new method for meaningful unsupervised discretization of numeric time series called Persist . The algorithm is based on the Kullback-Leibler divergence between the marginal and the self-transition probability distributions of the discretization symbols . Its performance is evaluated on both artificial and real life data in comparison to the most common discretization methods . Persist achieves significantly higher accuracy than existing static methods and is robust against noise . It also outperforms Hidden Markov Models for all but very simple cases .

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


Knowledge base maintenance using knowledge gap analysis As the web and e-business have proliferated , the practice of using customer facing knowledge bases to augment customer service and support operations has increased . This can be a very efficient , scalable and cost effective way to share knowledge . The effectiveness and cost savings are proportional to the utility of the information within the knowledge base and inversely proportional to the amount of labor required in maintaining the knowledge . To address this issue , we have developed an algorithm and methodology to increase the utility of the information within a knowledge base while greatly reducing the labor required . In this paper , we describe an implementation of an algorithm and methodology for comparing a knowledge base to a set of problem tickets to determine which categories and subcategories are not well addressed within the knowledge base . We utilize text clustering on problem ticket text to determine a set of problem categories . We then compare each knowledge base solution document to each problem category centroid using a cosine distance metric . The distance between the `` closest '' solution document and the corresponding centroid becomes the basis of that problem category 's `` knowledge gap '' . Our claim is that this gap metric serves as a useful method for quickly and automatically determining which problem categories have no relevant solutions in a knowledge base . We have implemented our approach , and we present the results of performing a knowledge gap analysis on a set of support center problem tickets .

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 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 B I O O O O O O O O O

database applications
information search and retrieval
knowledge management
text mining


Extracting collective probabilistic forecasts from web games Game sites on the World Wide Web draw people from around the world with specialized interests , skills , and knowledge . Data from the games often reflects the players ' expertise and will to win . We extract probabilistic forecasts from data obtained from three online games : the Hollywood Stock Exchange ( HSX ) , the Foresight Exchange ( FX ) , and the Formula One Pick Six ( F1P6 ) competition . We find that all three yield accurate forecasts of uncertain future events . In particular , prices of so-called `` movie stocks '' on HSX are good indicators of actual box office returns . Prices of HSX securities in Oscar , Emmy , and Grammy awards correlate well with observed frequencies of winning . FX prices are reliable indicators of future developments in science and technology . Collective predictions from players in the F1 competition serve as good forecasts of true race outcomes . In some cases , forecasts induced from game data are more reliable than expert opinions . We argue that web games naturally attract well-informed and well-motivated players , and thus offer a valuable and oft-overlooked source of high-quality data with significant predictive value .

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 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

collective probabilistic forecasts
foresight exchange
hollywood stock exchange

artificial markets
formula one pick six competition
knowledge discovery
world wide web games


Rule interestingness analysis using OLAP operations The problem of interestingness of discovered rules has been investigated by many researchers . The issue is that data mining algorithms often generate too many rules , which make it very hard for the user to find the interesting ones . Over the years many techniques have been proposed . However , few have made it to real-life applications . Since August 2004 , we have been working on a major application for Motorola . The objective is to find causes of cellular phone call failures from a large amount of usage log data . Class association rules have been shown to be suitable for this type of diagnostic data mining application . We were also able to put several existing interestingness methods to the test , which revealed some major shortcomings . One of the main problems is that most existing methods treat rules individually . However , we discovered that users seldom regard a single rule to be interesting by itself . A rule is only interesting in the context of some other rules . Furthermore , in many cases , each individual rule may not be interesting , but a group of them together can represent an important piece of knowledge . This led us to discover a deficiency of the current rule mining paradigm . Using non-zero minimum support and non-zero minimum confidence eliminates a large amount of context information , which makes rule analysis difficult . This paper proposes a novel approach to deal with all of these issues , which casts rule analysis as OLAP operations and general impression mining . This approach enables the user to explore the knowledge space to find useful knowledge easily and systematically . It also provides a natural framework for visualization . As an evidence of its effectiveness , our system , called Opportunity Map , based on these ideas has been deployed , and it is in daily use in Motorola for finding actionable knowledge from its engineering and other types of data sets .

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

class association rules
diagnostic data mining
interestingness analysis
olap


Unweaving a web of documents We develop an algorithmic framework to decompose a collection of time-stamped text documents into semantically coherent threads . Our formulation leads to a graph decomposition problem on directed acyclic graphs , for which we obtain three algorithms -- an exact algorithm that is based on minimum cost flow and two more efficient algorithms based on maximum matching and dynamic programming that solve specific versions of the graph decomposition problem . Applications of our algorithms include superior summarization of news search results , improved browsing paradigms for large collections of text-intensive corpora , and integration of time-stamped documents from a variety of sources . Experimental results based on over 250,000 news articles from a major newspaper over a period of four years demonstrate that our algorithms efficiently identify robust threads of varying lengths and time-spans .

O O O O 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 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

graph algorithms
information search and retrieval
news threads


Audience selection for on-line brand advertising : privacy-friendly social network targeting This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites , for the purpose of finding good audiences for brand advertising ( as opposed to click maximizing , for example ) . Targeting social-network neighbors resonates well with advertisers , and on-line browsing behavior data counterintuitively can allow the identification of good audiences anonymously . Besides being one of the first papers to our knowledge on data mining for on-line brand advertising , this paper makes several important contributions . We introduce a framework for evaluating brand audiences , in analogy to predictive-modeling holdout evaluation . We introduce methods for extracting quasi-social networks from data on visitations to social networking pages , without collecting any information on the identities of the browsers or the content of the social-network pages . We introduce measures of brand proximity in the network , and show that audiences with high brand proximity indeed show substantially higher brand affinity . Finally , we provide evidence that the quasi-social network embeds a true social network , which along with results from social theory offers one explanation for the increase in brand affinity of the selected audiences .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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-line advertising
predictive modeling
privacy
social networks
user-generated content


On-board analysis of uncalibrated data for a spacecraft at mars Analyzing data on-board a spacecraft as it is collected enables several advanced spacecraft capabilities , such as prioritizing observations to make the best use of limited bandwidth and reacting to dynamic events as they happen . In this paper , we describe how we addressed the unique challenges associated with on-board mining of data as it is collected : uncalibrated data , noisy observations , and severe limitations on computational and memory resources . The goal of this effort , which falls into the emerging application area of spacecraft-based data mining , was to study three specific science phenomena on Mars . Following previous work that used a linear support vector machine ( SVM ) on-board the Earth Observing 1 ( EO-1 ) spacecraft , we developed three data mining techniques for use on-board the Mars Odyssey spacecraft . These methods range from simple thresholding to state-of-the-art reduced-set SVM technology . We tested these algorithms on archived data in a flight software testbed . We also describe a significant , serendipitous science discovery of this data mining effort : the confirmation of a water ice annulus around the north polar cap of Mars . We conclude with a discussion on lessons learned in developing algorithms for use on-board a spacecraft .

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

on-board data mining
real-time data analysis
resource-constrained computing


Nonlinear adaptive distance metric learning for clustering A good distance metric is crucial for many data mining tasks . To learn a metric in the unsupervised setting , most metric learning algorithms project observed data to a low-dimensional manifold , where geometric relationships such as pairwise distances are preserved . It can be extended to the nonlinear case by applying the kernel trick , which embeds the data into a feature space by specifying the kernel function that computes the dot products between data points in the feature space . In this paper , we propose a novel unsupervised Nonlinear Adaptive Metric Learning algorithm , called NAML , which performs clustering and distance metric learning simultaneously . NAML firstmaps the data to a high-dimensional space through a kernel function ; then applies a linear projection to find a low-dimensional manifold where the separability of the data is maximized ; and finally performs clustering in the low-dimensional space . The performance of NAML depends on the selection of the kernel function and the projection . We show that the joint kernel learning , dimensionality reduction , and clustering can be formulated as a trace maximization problem , which can be solved via an iterative procedure in the EM framework . Experimental results demonstrated the efficacy of the proposed algorithm .

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


Mining high dimensional data for classifier knowledge We present in this paper the problem of discovering sets of attribute-value pairs in high dimensional data sets that are of interest not because of co-occurrence alone , but due to their value in serving as cores for potential classifiers of clusters . We present our algorithm in the context of a gene-expression dataset . Gene expression data , in most situations , is insufficient for clustering algorithms and any statistical inference because for 6000 + genes , typically only 10s and at most 100s of data points become available . It is difficult to use statistical techniques to design a classifier for such immensely under-specified data . The observed data , though statistically , insufficient contains some information about the domain . Our goal is to discover as much information about all potential classifiers as possible from the data and then summarize this knowledge . This summarization provides insights into the composition of potential classifiers . We present here algorithms and methods for mining a high dimensional data set , exemplified by a gene expression data set , for mining such information .

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


Real world performance of association rule algorithms This study compares five well-known association rule algorithms using three real-world datasets and an artificial dataset . The experimental results confirm the performance improvements previously claimed by the authors on the artificial data , but some of these gains do not carry over to the real datasets , indicating overfitting of the algorithms to the IBM artificial dataset . More importantly , we found that the choice of algorithm only matters at support levels that generate more rules than would be useful in practice . For support levels that generate less than 1,000,000 rules , which is much more than humans can handle and is sufficient for prediction purposes where data is loaded into RAM , Apriori finishes processing in less than 10 minutes . On our datasets , we observed super-exponential growth in the number of rules . On one of our datasets , a 0.02 % change in the support increased the number of rules from less than a million to over a billion , implying that outside a very narrow range of support values , the choice of algorithm is irrelevant .

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

affinity analysis
association rules
benchmark
comparisons
frequent itemsets
market basket analysis


The data mining approach to automated software testing In today 's industry , the design of software tests is mostly based on the testers ' expertise , while test automation tools are limited to execution of pre-planned tests only . Evaluation of test outputs is also associated with a considerable effort by human testers who often have imperfect knowledge of the requirements specification . Not surprisingly , this manual approach to software testing results in heavy losses to the world 's economy . The costs of the so-called `` catastrophic '' software failures ( such as Mars Polar Lander shutdown in 1999 ) are even hard to measure . In this paper , we demonstrate the potential use of data mining algorithms for automated induction of functional requirements from execution data . The induced data mining models of tested software can be utilized for recovering missing and incomplete specifications , designing a minimal set of regression tests , and evaluating the correctness of software outputs when testing new , potentially flawed releases of the system . To study the feasibility of the proposed approach , we have applied a novel data mining algorithm called Info-Fuzzy Network ( IFN ) to execution data of a general-purpose code for solving partial differential equations . After being trained on a relatively small number of randomly generated input-output examples , the model constructed by the IFN algorithm has shown a clear capability to discriminate between correct and faulty versions of the program .

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

finite element solver
info-fuzzy networks
input-output analysis
regression testing
testing tools


Building connected neighborhood graphs for isometric data embedding Neighborhood graph construction is usually the first step in algorithms for isometric data embedding and manifold learning that cope with the problem of projecting high dimensional data to a low space . This paper begins by explaining the algorithmic fundamentals of techniques for isometric data embedding and derives a general classification of these techniques . We will see that the nearest neighbor approaches commonly used to construct neighborhood graphs do not guarantee connectedness of the constructed neighborhood graphs and , consequently , may cause an algorithm fail to project data to a single low dimensional coordinate system . In this paper , we review three existing methods to construct k-edge-connected neighborhood graphs and propose a new method to construct k-connected neighborhood graphs . These methods are applicable to a wide range of data including data distributed among clusters . Their features are discussed and compared through experiments .

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

dimensionality reduction
graph connectivity


Privacy-preserving k-means clustering over vertically partitioned data Privacy and security concerns can prevent sharing of data , derailing data mining projects . Distributed knowledge discovery , if done correctly , can alleviate this problem . The key is to obtain valid results , while providing guarantees on the ( non ) disclosure of data . We present a method for k-means clustering when different sites contain different attributes for a common set of entities . Each site learns the cluster of each entity , but learns nothing about the attributes at other sites .

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


Detecting graph-based spatial outliers : algorithms and applications ( a summary of results ) Identification of outliers can lead to the discovery of unexpected , interesting , and useful knowledge . Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets , where a distance metric is available . In this paper , we focus on detecting spatial outliers in graph structured data sets . We define statistical tests , analyze the statistical foundation underlying our approach , design several fast algorithms to detect spatial outliers , and provide a cost model for outlier detection procedures . In addition , we provide experimental results from the application of our algorithms on a Minneapolis-St . Paul ( Twin Cities ) traffic dataset to show their effectiveness and usefulness .

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Balanced allocation with succinct representation Motivated by applications in guaranteed delivery in computational advertising , we consider the general problem of balanced allocation in a bipartite supply-demand setting . Our formulation captures the notion of deviation from being balanced by a convex penalty function . While this formulation admits a convex programming solution , we strive for more robust and scalable algorithms . For the case of L1 penalty functions we obtain a simple combinatorial algorithm based on min-cost flow in graphs and show how to precompute a linear amount of information such that the allocation along any edge can be approximated in constant time . We then extend our combinatorial solution to any convex function by solving a convex cost flow . These scalable methods may have applications in other contexts stipulating balanced allocation . We study the performance of our algorithms on large real-world graphs and show that they are efficient , scalable , and robust in practice .

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Feature selection in unsupervised learning via evolutionary search


Discovering evolutionary theme patterns from text : an exploration of temporal text mining Temporal Text Mining ( TTM ) is concerned with discovering temporal patterns in text information collected over time . Since most text information bears some time stamps , TTM has many applications in multiple domains , such as summarizing events in news articles and revealing research trends in scientific literature . In this paper , we study a particular TTM task -- discovering and summarizing the evolutionary patterns of themes in a text stream . We define this new text mining problem and present general probabilistic methods for solving this problem through ( 1 ) discovering latent themes from text ; ( 2 ) constructing an evolution graph of themes ; and ( 3 ) analyzing life cycles of themes . Evaluation of the proposed methods on two different domains ( i.e. , news articles and literature ) shows that the proposed methods can discover interesting evolutionary theme patterns effectively .

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evolutionary theme patterns
temporal text mining


Predicting bounce rates in sponsored search advertisements This paper explores an important and relatively unstudied quality measure of a sponsored search advertisement : bounce rate . The bounce rate of an ad can be informally defined as the fraction of users who click on the ad but almost immediately move on to other tasks . A high bounce rate can lead to poor advertiser return on investment , and suggests search engine users may be having a poor experience following the click . In this paper , we first provide quantitative analysis showing that bounce rate is an effective measure of user satisfaction . We then address the question , can we predict bounce rate by analyzing the features of the advertisement ? An affirmative answer would allow advertisers and search engines to predict the effectiveness and quality of advertisements before they are shown . We propose solutions to this problem involving large-scale learning methods that leverage features drawn from ad creatives in addition to their keywords and landing pages .

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applications
machine learning
online advertisement