Generating English summaries of time series data using the Gricean maxims We are developing technology for generating English textual summaries of time-series data , in three domains : weather forecasts , gas-turbine sensor readings , and hospital intensive care data . Our weather-forecast generator is currently operational and being used daily by a meteorological company . We generate summaries in three steps : ( a ) selecting the most important trends and patterns to communicate ; ( b ) mapping these patterns onto words and phrases ; and ( c ) generating actual texts based on these words and phrases . In this paper we focus on the first step , ( a ) , selecting the information to communicate , and describe how we perform this using modified versions of standard data analysis algorithms such as segmentation . The modifications arose out of empirical work with users and domain experts , and in fact can all be regarded as applications of the Gricean maxims of Quality , Quantity , Relevance , and Manner , which describe how a cooperative speaker should behave in order to help a hearer correctly interpret a text . The Gricean maxims are perhaps a key element of adapting data analysis algorithms for effective communication of information to human users , and should be considered by other researchers interested in communicating data to human users .

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

natural language processing
summarization


Learning subspace kernels for classification Kernel methods have been applied successfully in many data mining tasks . Subspace kernel learning was recently proposed to discover an effective low-dimensional subspace of a kernel feature space for improved classification . In this paper , we propose to construct a subspace kernel using the Hilbert-Schmidt Independence Criterion ( HSIC ) . We show that the optimal subspace kernel can be obtained efficiently by solving an eigenvalue problem . One limitation of the existing subspace kernel learning formulations is that the kernel learning and classification are independent and the subspace kernel may not be optimally adapted for classification . To overcome this limitation , we propose a joint optimization framework , in which we learn the subspace kernel and subsequent classifiers simultaneously . In addition , we propose a novel learning formulation that extracts an uncorrelated subspace kernel to reduce the redundant information in a subspace kernel . Following the idea from multiple kernel learning , we extend the proposed formulations to the case when multiple kernels are available and need to be combined . We show that the integration of subspace kernels can be formulated as a semidefinite program ( SDP ) which is computationally expensive . To improve the efficiency of the SDP formulation , we propose an equivalent semi-infinite linear program ( SILP ) formulation which can be solved efficiently by the column generation technique . Experimental results on a collection of benchmark data sets demonstrate the effectiveness of the proposed algorithms .

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

hilbert-schmidt independence criterion
support vector machines


Redundancy based feature selection for microarray data In gene expression microarray data analysis , selecting a small number of discriminative genes from thousands of genes is an important problem for accurate classification of diseases or phenotypes . The problem becomes particularly challenging due to the large number of features ( genes ) and small sample size . Traditional gene selection methods often select the top-ranked genes according to their individual discriminative power without handling the high degree of redundancy among the genes . Latest research shows that removing redundant genes among selected ones can achieve a better representation of the characteristics of the targeted phenotypes and lead to improved classification accuracy . Hence , we study in this paper the relationship between feature relevance and redundancy and propose an efficient method that can effectively remove redundant genes . The efficiency and effectiveness of our method in comparison with representative methods has been demonstrated through an empirical study using public microarray data sets .

O O O O O B 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 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 B I O O


Reconstructing chemical reaction networks : data mining meets system identification We present an approach to reconstructing chemical reaction networks from time series measurements of the concentrations of the molecules involved . Our solution strategy combines techniques from numerical sensitivity analysis and probabilistic graphical models . By modeling a chemical reaction system as a Markov network ( undirected graphical model ) , we show how systematically probing for sensitivities between molecular species can identify the topology of the network . Given the topology , our approach next uses detailed sensitivity profiles to characterize properties of reactions such as reversibility , enzyme-catalysis , and the precise stoichiometries of the reactants and products . We demonstrate applications to reconstructing key biological systems including the yeast cell cycle . In addition to network reconstruction , our algorithm finds applications in model reduction and model comprehension . We argue that our reconstruction algorithm can serve as an important primitive for data mining in systems biology applications .

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

graphical models
network reconstruction
systems biology

markov networks
ordinary differential equations


Statistical change detection for multi-dimensional data This paper deals with detecting change of distribution in multi-dimensional data sets . For a given baseline data set and a set of newly observed data points , we define a statistical test called the density test for deciding if the observed data points are sampled from the underlying distribution that produced the baseline data set . We define a test statistic that is strictly distribution-free under the null hypothesis . Our experimental results show that the density test has substantially more power than the two existing methods for multi-dimensional change detection .

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

database applications
kernel density estimation


Large linear classification when data can not fit in memory Recent advances in linear classification have shown that for applications such as document classification , the training can be extremely efficient . However , most of the existing training methods are designed by assuming that data can be stored in the computer memory . These methods can not be easily applied to data larger than the memory capacity due to the random access to the disk . We propose and analyze a block minimization framework for data larger than the memory size . At each step a block of data is loaded from the disk and handled by certain learning methods . We investigate two implementations of the proposed framework for primal and dual SVMs , respectively . As data can not fit in memory , many design considerations are very different from those for traditional algorithms . Experiments using data sets 20 times larger than the memory demonstrate the effectiveness of the proposed method .

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


A two-way visualization method for clustered data We describe a novel approach to the visualization of hierarchical clustering that superimposes the classical dendrogram over a fully synchronized low-dimensional embedding , thereby gaining the benefits of both approaches . In a single image one can view all the clusters , examine the relations between them and study many of their properties . The method is based on an algorithm for low-dimensional embedding of clustered data , with the property that separation between all clusters is guaranteed , regardless of their nature . In particular , the algorithm was designed to produce embeddings that strictly adhere to a given hierarchical clustering of the data , so that every two disjoint clusters in the hierarchy are drawn separately .

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


Multi-level organization and summarization of the discovered rules


Catching the drift : learning broad matches from clickthrough data Identifying similar keywords , known as broad matches , is an important task in online advertising that has become a standard feature on all major keyword advertising platforms . Effective broad matching leads to improvements in both relevance and monetization , while increasing advertisers ' reach and making campaign management easier . In this paper , we present a learning-based approach to broad matching that is based on exploiting implicit feedback in the form of advertisement clickthrough logs . Our method can utilize arbitrary similarity functions by incorporating them as features . We present an online learning algorithm , Amnesiac Averaged Perceptron , that is highly efficient yet able to quickly adjust to the rapidly-changing distributions of bidded keywords , advertisements and user behavior . Experimental results obtained from ( 1 ) historical logs and ( 2 ) live trials on a large-scale advertising platform demonstrate the effectiveness of the proposed algorithm and the overall success of our approach in identifying high-quality broad match mappings .

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

information search and retrieval
keyword similarity
keyword-based advertising
on-line information services


A distributed learning framework for heterogeneous data sources We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites have diverse , possibly overlapping subsets of features . Our framework decouples data privacy issues from knowledge integration issues by requiring the individual sites to share only privacy-safe probabilistic models of the local data , which are then integrated to obtain a global probabilistic model based on the union of the features available at all the sites . We provide a mathematical formulation of the model integration problem using the maximum likelihood and maximum entropy principles and describe iterative algorithms that are guaranteed to converge to the optimal solution . For certain commonly occurring special cases involving hierarchically ordered feature sets or conditional independence , we obtain closed form solutions and use these to propose an efficient alternative scheme by recursive decomposition of the model integration problem . To address interpretability concerns , we also present a modified formulation where the global model is assumed to belong to a specified parametric family . Finally , to highlight the generality of our framework , we provide empirical results for various learning tasks such as clustering and classification on different kinds of datasets consisting of continuous vector , categorical and directional attributes . The results show that high quality global models can be obtained without much loss of privacy .

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

distributed learning
heterogeneous data sources
learning
privacy
probabilistic models


Overlapping experiment infrastructure : more , better , faster experimentation At Google , experimentation is practically a mantra ; we evaluate almost every change that potentially affects what our users experience . Such changes include not only obvious user-visible changes such as modifications to a user interface , but also more subtle changes such as different machine learning algorithms that might affect ranking or content selection . Our insatiable appetite for experimentation has led us to tackle the problems of how to run more experiments , how to run experiments that produce better decisions , and how to run them faster . In this paper , we describe Google 's overlapping experiment infrastructure that is a key component to solving these problems . In addition , because an experiment infrastructure alone is insufficient , we also discuss the associated tools and educational processes required to use it effectively . We conclude by describing trends that show the success of this overall experimental environment . While the paper specifically describes the experiment system and experimental processes we have in place at Google , we believe they can be generalized and applied by any entity interested in using experimentation to improve search engines and other web applications .

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

a/b testing
controlled experiments
multivariable testing
website testing


A DEA approach for model combination This paper proposes a novel Data Envelopment Analysis ( DEA ) based approach for model combination . We first prove that for the 2-class classification problems DEA models identify the same convex hull as the popular ROC analysis used for model combination . For general k-class classifiers , we then develop a DEA-based method to combine multiple classifiers . Experiments show that the method outperforms other benchmark methods and suggest that DEA can be a promising tool for model combination .

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

data envelopment analysis
model combination
roc


Probabilistic frequent itemset mining in uncertain databases Probabilistic frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard `` certain '' transaction databases . The consideration of existential uncertainty of item ( sets ) , indicating the probability that an item ( set ) occurs in a transaction , makes traditional techniques inapplicable . In this paper , we introduce new probabilistic formulations of frequent itemsets based on possible world semantics . In this probabilistic context , an itemset X is called frequent if the probability that X occurs in at least minSup transactions is above a given threshold τ . To the best of our knowledge , this is the first approach addressing this problem under possible worlds semantics . In consideration of the probabilistic formulations , we present a framework which is able to solve the Probabilistic Frequent Itemset Mining ( PFIM ) problem efficiently . An extensive experimental evaluation investigates the impact of our proposed techniques and shows that our approach is orders of magnitude faster than straight-forward approaches .

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

frequent itemset mining
uncertain databases

probabilistic data
probabilistic frequent itemsets


A mixture model for contextual text mining Contextual text mining is concerned with extracting topical themes from a text collection with context information ( e.g. , time and location ) and comparing\/analyzing the variations of themes over different contexts . Since the topics covered in a document are usually related to the context of the document , analyzing topical themes within context can potentially reveal many interesting theme patterns . In this paper , we generalize some of these models proposed in the previous work and we propose a new general probabilistic model for contextual text mining that can cover several existing models as special cases . Specifically , we extend the probabilistic latent semantic analysis ( PLSA ) model by introducing context variables to model the context of a document . The proposed mixture model , called contextual probabilistic latent semantic analysis ( CPLSA ) model , can be applied to many interesting mining tasks , such as temporal text mining , spatiotemporal text mining , author-topic analysis , and cross-collection comparative analysis . Empirical experiments show that the proposed mixture model can discover themes and their contextual variations effectively .

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

context
contextual text mining
mixture model

clustering
em algorithm
information search and retrieval
theme pattern


Sampling-based sequential subgroup mining Subgroup discovery is a learning task that aims at finding interesting rules from classified examples . The search is guided by a utility function , trading off the coverage of rules against their statistical unusualness . One shortcoming of existing approaches is that they do not incorporate prior knowledge . To this end a novel generic sampling strategy is proposed . It allows to turn pattern mining into an iterative process . In each iteration the focus of subgroup discovery lies on those patterns that are unexpected with respect to prior knowledge and previously discovered patterns . The result of this technique is a small diverse set of understandable rules that characterise a specified property of interest . As another contribution this article derives a simple connection between subgroup discovery and classifier induction . For a popular utility function this connection allows to apply any standard rule induction algorithm to the task of subgroup discovery after a step of stratified resampling . The proposed techniques are empirically compared to state of the art subgroup discovery algorithms .

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 B 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 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 B I O O

prior knowledge
sampling
subgroup discovery


Online multiscale dynamic topic models We propose an online topic model for sequentially analyzing the time evolution of topics in document collections . Topics naturally evolve with multiple timescales . For example , some words may be used consistently over one hundred years , while other words emerge and disappear over periods of a few days . Thus , in the proposed model , current topic-specific distributions over words are assumed to be generated based on the multiscale word distributions of the previous epoch . Considering both the long-timescale dependency as well as the short-timescale dependency yields a more robust model . We derive efficient online inference procedures based on a stochastic EM algorithm , in which the model is sequentially updated using newly obtained data ; this means that past data are not required to make the inference . We demonstrate the effectiveness of the proposed method in terms of predictive performance and computational efficiency by examining collections of real documents with timestamps .

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


Applying collaborative filtering techniques to movie search for better ranking and browsing We propose a new ranking method , which combines recommender systems with information search tools for better search and browsing . Our method uses a collaborative filtering algorithm to generate personal item authorities for each user and combines them with item proximities for better ranking . To demonstrate our approach , we build a prototype movie search and browsing engine called MAD6 ( Movies , Actors and Directors ; 6 degrees of separation ) . We conduct offline and online tests of our ranking algorithm . For offline testing , we use Yahoo ! Search queries that resulted in a click on a Yahoo ! Movies or Internet Movie Database ( IMDB ) movie URL . Our online test involved 44 Yahoo ! employees providing subjective assessments of results quality . In both tests , our ranking methods show significantly better recall and quality than IMDB search and Yahoo ! Movies current search .

O B I 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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
movie search
recommender systems

information retrieval
performance evaluation
search ranking


Bursty and hierarchical structure in streams A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time . E-mail and news articles are two natural examples of such streams , each characterized by topics that appear , grow in intensity for a period of time , and then fade away . The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale . Underlying much of the text mining work in this area is the following intuitive premise -- that the appearance of a topic in a document stream is signaled by a `` burst of activity , '' with certain features rising sharply in frequency as the topic emerges . The goal of the present work is to develop a formal approach for modeling such `` bursts , '' in such a way that they can be robustly and efficiently identified , and can provide an organizational framework for analyzing the underlying content . The approach is based on modeling the stream using an infinite-state automaton , in which bursts appear naturally as state transitions ; in some ways , it can be viewed as drawing an analogy with models from queueing theory for bursty network traffic . The resulting algorithms are highly efficient , and yield a nested representation of the set of bursts that imposes a hierarchical structure on the overall stream . Experiments with e-mail and research paper archives suggest that the resulting structures have a natural meaning in terms of the content that gave rise to them .

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


A sequential sampling algorithm for a general class of utility criteria


Mining web logs for prediction models in WWW caching and prefetching Web caching and prefetching are well known strategies for improving the performance of Internet systems . When combined with web log mining , these strategies can decide to cache and prefetch web documents with higher accuracy . In this paper , we present an application of web log mining to obtain web-document access patterns and use these patterns to extend the well-known GDSF caching policies and prefetching policies . Using real web logs , we show that this application of data mining can achieve dramatic improvement to web-access performance .

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

application to caching and prefetching on the www


Hierarchical model-based clustering of large datasets through fractionation and refractionation The goal of clustering is to identify distinct groups in a dataset . Compared to non-parametric clustering methods like complete linkage , hierarchical model-based clustering has the advantage of offering a way to estimate the number of groups present in the data . However , its computational cost is quadratic in the number of items to be clustered , and it is therefore not applicable to large problems . We review an idea called Fractionation , originally conceived by Cutting , Karger , Pedersen and Tukey for non-parametric hierarchical clustering of large datasets , and describe an adaptation of Fractionation to model-based clustering . A further extension , called Refractionation , leads to a procedure that can be successful even in the difficult situation where there are large numbers of small groups .

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


Quantification and semi-supervised classification methods for handling changes in class distribution In realistic settings the prevalence of a class may change after a classifier is induced and this will degrade the performance of the classifier . Further complicating this scenario is the fact that labeled data is often scarce and expensive . In this paper we address the problem where the class distribution changes and only unlabeled examples are available from the new distribution . We design and evaluate a number of methods for coping with this problem and compare the performance of these methods . Our quantification-based methods estimate the class distribution of the unlabeled data from the changed distribution and adjust the original classifier accordingly , while our semi-supervised methods build a new classifier using the examples from the new ( unlabeled ) distribution which are supplemented with predicted class values . We also introduce a hybrid method that utilizes both quantification and semi-supervised learning . All methods are evaluated using accuracy and F-measure on a set of benchmark data sets . Our results demonstrate that our methods yield substantial improvements in accuracy and F-measure .

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

class distribution
classification
quantification


Extracting decision trees from trained neural networks Neural Networks are successful in acquiring hidden knowledge in datasets . Their biggest weakness is that the knowledge they acquire is represented in a form not understandable to humans . Researchers tried to address this problem by extracting rules from trained Neural Networks . Most of the proposed rule extraction methods required specialized type of Neural Networks ; some required binary inputs and some were computationally expensive . Craven proposed extracting MofN type Decision Trees from Neural Networks . We believe MofN type Decision Trees are only good for MofN type problems and trees created for regular high dimensional real world problems may be very complex . In this paper , we introduced a new method for extracting regular C4 .5 like Decision Trees from trained Neural Networks . We showed that the new method ( DecText ) is effective in extracting high fidelity trees from trained networks . We also introduced a new discretization technique to make DecText be able to handle continuous features and a new pruning technique for finding simplest tree with the highest fidelity .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 genealogy : uncovering the flow of ideas in non-hyperlinked document databases We now have incrementally-grown databases of text documents ranging back for over a decade in areas ranging from personal email , to news-articles and conference proceedings . While accessing individual documents is easy , methods for overviewing and understanding these collections as a whole are lacking in number and in scope . In this paper , we address one such global analysis task , namely the problem of automatically uncovering how ideas spread through the collection over time . We refer to this problem as Information Genealogy . In contrast to bibliometric methods that are limited to collections with explicit citation structure , we investigate content-based methods requiring only the text and timestamps of the documents . In particular , we propose a language-modeling approach and a likelihood ratio test to detect influence between documents in a statistically well-founded way . Furthermore , we show how this method can be used to infer citation graphs and to identify the most influential documents in the collection . Experiments on the NIPS conference proceedings and the Physics ArXiv show that our method is more effective than methods based on document similarity .

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

citation inference
language models
miscellaneous
temporal data
text mining


An integrated machine learning approach to stroke prediction Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States . Accurate prediction of stroke is highly valuable for early intervention and treatment . In this study , we compare the Cox proportional hazards model with a machine learning approach for stroke prediction on the Cardiovascular Health Study ( CHS ) dataset . Specifically , we consider the common problems of data imputation , feature selection , and prediction in medical datasets . We propose a novel automatic feature selection algorithm that selects robust features based on our proposed heuristic : conservative mean . Combined with Support Vector Machines ( SVMs ) , our proposed feature selection algorithm achieves a greater area under the ROC curve ( AUC ) as compared to the Cox proportional hazards model and L1 regularized Cox feature selection algorithm . Furthermore , we present a margin-based censored regression algorithm that combines the concept of margin-based classifiers with censored regression to achieve a better concordance index than the Cox model . Overall , our approach outperforms the current state-of-the-art in both metrics of AUC and concordance index . In addition , our work has also identified potential risk factors that have not been discovered by traditional approaches . Our method can be applied to clinical prediction of other diseases , where missing data are common and risk factors are not well understood .

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

concordance index
feature selection
prediction
roc
stroke

benchmark
classification
data analysis
healthcare
medical data analysis
stroke prediction
svm


Automatic record linkage using seeded nearest neighbor and support vector machine classification The task of linking databases is an important step in an increasing number of data mining projects , because linked data can contain information that is not available otherwise , or that would require time-consuming and expensive collection of specific data . The aim of linking is to match and aggregate all records that refer to the same entity . One of the major challenges when linking large databases is the efficient and accurate classification of record pairs into matches and non-matches . While traditionally classification was based on manually-set thresholds or on statistical procedures , many of the more recently developed classification methods are based on supervised learning techniques . They therefore require training data , which is often not available in real world situations or has to be prepared manually , an expensive , cumbersome and time-consuming process . The author has previously presented a novel two-step approach to automatic record pair classification ( 6 , 7 ) . In the first step of this approach , training examples of high quality are automatically selected from the compared record pairs , and used in the second step to train a support vector machine ( SVM ) classifier . Initial experiments showed the feasibility of the approach , achieving results that outperformed k-means clustering . In this paper , two variations of this approach are presented . The first is based on a nearest-neighbour classifier , while the second improves a SVM classifier by iteratively adding more examples into the training sets . Experimental results show that this two-step approach can achieve better classification results than other unsupervised approaches .

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 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

data linkage
data matching
deduplication
entity resolution
nearest neighbour


Mining comparable bilingual text corpora for cross-language information integration Integrating information in multiple natural languages is a challenging task that often requires manually created linguistic resources such as a bilingual dictionary or examples of direct translations of text . In this paper , we propose a general cross-lingual text mining method that does not rely on any of these resources , but can exploit comparable bilingual text corpora to discover mappings between words and documents in different languages . Comparable text corpora are collections of text documents in different languages that are about similar topics ; such text corpora are often naturally available ( e.g. , news articles in different languages published in the same time period ) . The main idea of our method is to exploit frequency correlations of words in different languages in the comparable corpora and discover mappings between words in different languages . Such mappings can then be used to further discover mappings between documents in different languages , achieving cross-lingual information integration . Evaluation of the proposed method on a 120MB Chinese-English comparable news collection shows that the proposed method is effective for mapping words and documents in English and Chinese . Since our method only relies on naturally available comparable corpora , it is generally applicable to any language pairs as long as we have comparable corpora .

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

cross-lingual text mining
document alignment
frequency correlation
information search and retrieval


Incremental maintenance of quotient cube for median Data cube pre-computation is an important concept for supporting OLAP ( Online Analytical Processing ) and has been studied extensively . It is often not feasible to compute a complete data cube due to the huge storage requirement . Recently proposed quotient cube addressed this issue through a partitioning method that groups cube cells into equivalence partitions . Such an approach is not only useful for distributive aggregate functions such as SUM but can also be applied to the holistic aggregate functions like MEDIAN . Maintaining a data cube for holistic aggregation is a hard problem since its difficulty lies in the fact that history tuple values must be kept in order to compute the new aggregate when tuples are inserted or deleted . The quotient cube makes the problem harder since we also need to maintain the equivalence classes . In this paper , we introduce two techniques called addset data structure and sliding window to deal with this problem . We develop efficient algorithms for maintaining a quotient cube with holistic aggregation functions that takes up reasonably small storage space . Performance study shows that our algorithms are effective , efficient and scalable over large databases .

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


From run-time behavior to usage scenarios : an interaction-pattern mining approach A key challenge facing IT organizations today is their evolution towards adopting e-business practices that gives rise to the need for reengineering their underlying software systems . Any reengineering effort has to be aware of the functional requirements of the subject system , in order not to violate the integrity of its intended uses . However , as software systems get regularly maintained throughout their lifecycle , the documentation of their requirements often become obsolete or get lost . To address this problem of `` software requirements loss '' , we have developed an interaction-pattern mining method for the recovery of functional requirements as usage scenarios . Our method analyzes traces of the run-time system-user interaction to discover frequently recurring patterns ; these patterns correspond to the functionality currently exercised by the system users , represented as usage scenarios . The discovered scenarios provide the basis for reengineering the software system into web-accessible components , each one supporting one of the discovered scenarios . In this paper , we describe IPM2 , our interaction-pattern discovery algorithm , we illustrate it with a case study from a real application and we give an overview of the reengineering process in the context of which it is employed .

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

interaction reengineering
run-time behavior analysis
sequential pattern mining
software engineering
software requirements recovery


Local decomposition for rare class analysis Given its importance , the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature . However , the rare-class problem remains a critical challenge , because there is no natural way developed for handling imbalanced class distributions . This paper thus fills this crucial void by developing a method for Classification using lOcal clusterinG ( COG ) . Specifically , for a data set with an imbalanced class distribution , we perform clustering within each large class and produce sub-classes with relatively balanced sizes . Then , we apply traditional supervised learning algorithms , such as Support Vector Machines ( SVMs ) , for classification . Indeed , our experimental results on various real-world data sets show that our method produces significantly higher prediction accuracies on rare classes than state-of-the-art methods . Furthermore , we show that COG can also improve the performance of traditional supervised learning algorithms on data sets with balanced class distributions .

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 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

k-means clustering support vector machines


Tensor-CUR decompositions for tensor-based data Motivated by numerous applications in which the data may be modeled by a variable subscripted by three or more indices , we develop a tensor-based extension of the matrix CUR decomposition . The tensor-CUR decomposition is most relevant as a data analysis tool when the data consist of one mode that is qualitatively different than the others . In this case , the tensor-CUR decomposition approximately expresses the original data tensor in terms of a basis consisting of underlying subtensors that are actual data elements and thus that have natural interpretation in terms ofthe processes generating the data . In order to demonstrate the general applicability of this tensor decomposition , we apply it to problems in two diverse domains of data analysis : hyperspectral medical image analysis and consumer recommendation system analysis . In the hyperspectral data application , the tensor-CUR decomposition is used to compress the data , and we show that classification quality is not substantially reduced even after substantial data compression . In the recommendation system application , the tensor-CUR decomposition is used to reconstruct missing entries in a user-product-product preference tensor , and we show that high quality recommendations can be made on the basis of a small number of basis users and a small number of product-product comparisons from a new user .

O O O O O O O O O O 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 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

cur decomposition
recommendation system analysis

hyperspectral image analysis
miscellaneous
tensor cur


A generalized framework for mining spatio-temporal patterns in scientific data In this paper , we present a general framework to discover spatial associations and spatio-temporal episodes for scientific datasets . In contrast to previous work in this area , features are modeled as geometric objects rather than points . We define multiple distance metrics that take into account objects ' extent and thus are more robust in capturing the influence of an object on other objects in spatial neighborhood . We have developed algorithms to discover four different types of spatial object interaction ( association ) patterns . We also extend our approach to accommodate temporal information and propose a simple algorithm to derive spatio-temporal episodes . We show that such episodes can be used to reason about critical events . We evaluate our framework on real datasets to demonstrate its efficacy . The datasets originate from two different areas : Computational Molecular Dynamics and Computational Fluid Flow . We present results highlighting the importance of the identified patterns and episodes by using knowledge from the underlying domains . We also show that the proposed algorithms scale linearly with respect to the dataset size .

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

spatial object association
spatio-temporal association/episode


CLOSET + : searching for the best strategies for mining frequent closed itemsets Mining frequent closed itemsets provides complete and non-redundant results for frequent pattern analysis . Extensive studies have proposed various strategies for efficient frequent closed itemset mining , such as depth-first search vs. breadthfirst search , vertical formats vs. horizontal formats , tree-structure vs. other data structures , top-down vs. bottom-up traversal , pseudo projection vs. physical projection of conditional database , etc. . It is the right time to ask `` what are the pros and cons of the strategies ? '' and `` what and how can we pick and integrate the best strategies to achieve higher performance in general cases ? `` In this study , we answer the above questions by a systematic study of the search strategies and develop a winning algorithm CLOSET + . CLOSET + integrates the advantages of the previously proposed effective strategies as well as some ones newly developed here . A thorough performance study on synthetic and real data sets has shown the advantages of the strategies and the improvement of CLOSET + over existing mining algorithms , including CLOSET , CHARM and OP , in terms of runtime , memory usage and scalability .

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

association rules
mining methods and algorithms


The offset tree for learning with partial labels We present an algorithm , called the Offset Tree , for learning to make decisions in situations where the payoff of only one choice is observed , rather than all choices . The algorithm reduces this setting to binary classification , allowing one to reuse any existing , fully supervised binary classification algorithm in this partial information setting . We show that the Offset Tree is an optimal reduction to binary classification . In particular , it has regret at most ( k-1 ) times the regret of the binary classifier it uses ( where k is the number of choices ) , and no reduction to binary classification can do better . This reduction is also computationally optimal , both at training and test time , requiring just O ( log2 k ) work to train on an example or make a prediction . Experiments with the Offset Tree show that it generally performs better than several alternative approaches .

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

associative reinforcement learning
contextual bandits
interactive learning


Efficient handling of high-dimensional feature spaces by randomized classifier ensembles Handling massive datasets is a difficult problem not only due to prohibitively large numbers of entries but in some cases also due to the very high dimensionality of the data . Often , severe feature selection is performed to limit the number of attributes to a manageable size , which unfortunately can lead to a loss of useful information . Feature space reduction may well be necessary for many stand-alone classifiers , but recent advances in the area of ensemble classifier techniques indicate that overall accurate classifier aggregates can be learned even if each individual classifier operates on incomplete `` feature view '' training data , i.e. , such where certain input attributes are excluded . In fact , by using only small random subsets of features to build individual component classifiers , surprisingly accurate and robust models can be created . In this work we demonstrate how these types of architectures effectively reduce the feature space for submodels and groups of sub-models , which lends itself to efficient sequential and\/or parallel implementations . Experiments with a randomized version of Adaboost are used to support our arguments , using the text classification task as an example .

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


Acclimatizing Taxonomic Semantics for Hierarchical Content Classification Hierarchical models have been shown to be effective in content classification . However , we observe through empirical study that the performance of a hierarchical model varies with given taxonomies ; even a semantically sound taxonomy has potential to change its structure for better classification . By scrutinizing typical cases , we elucidate why a given semantics-based hierarchy does not work well in content classification , and how it could be improved for accurate hierarchical classification . With these understandings , we propose effective localized solutions that modify the given taxonomy for accurate hierarchical classification . We conduct extensive experiments on both toy and real-world data sets , report improved performance and interesting findings , and provide further analysis of algorithmic issues such as time complexity , robustness , and sensitivity to the number of features .

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

hierarchical modeling
taxonomy adjustment
text classification


Webpage understanding : an integrated approach Recent work has shown the effectiveness of leveraging layout and tag-tree structure for segmenting webpages and labeling HTML elements . However , how to effectively segment and label the text contents inside HTML elements is still an open problem . Since many text contents on a webpage are often text fragments and not strictly grammatical , traditional natural language processing techniques , that typically expect grammatical sentences , are no longer directly applicable . In this paper , we examine how to use layout and tag-tree structure in a principled way to help understand text contents on webpages . We propose to segment and label the page structure and the text content of a webpage in a joint discriminative probabilistic model . In this model , semantic labels of page structure can be leveraged to help text content understanding , and semantic labels ofthe text phrases can be used in page structure understanding tasks such as data record detection . Thus , integration of both page structure and text content understanding leads to an integrated solution of webpage understanding . Experimental results on research homepage extraction show the feasibility and promise of our approach .

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

conditional random fields
text processing


SPIN : mining maximal frequent subgraphs from graph databases One fundamental challenge for mining recurring subgraphs from semi-structured data sets is the overwhelming abundance of such patterns . In large graph databases , the total number of frequent subgraphs can become too large to allow a full enumeration using reasonable computational resources . In this paper , we propose a new algorithm that mines only maximal frequent subgraphs , i.e. subgraphs that are not a part of any other frequent subgraphs . This may exponentially decrease the size of the output set in the best case ; in our experiments on practical data sets , mining maximal frequent subgraphs reduces the total number of mined patterns by two to three orders of magnitude . Our method first mines all frequent trees from a general graph database and then reconstructs all maximal subgraphs from the mined trees . Using two chemical structure benchmarks and a set of synthetic graph data sets , we demonstrate that , in addition to decreasing the output size , our algorithm can achieve a five-fold speed up over the current state-of-the-art subgraph mining algorithms .

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


Time and sample efficient discovery of Markov blankets and direct causal relations Data Mining with Bayesian Network learning has two important characteristics : under conditions learned edges between variables correspond to casual influences , and second , for every variable T in the network a special subset ( Markov Blanket ) identifiable by the network is the minimal variable set required to predict T. However , all known algorithms learning a complete BN do not scale up beyond a few hundred variables . On the other hand , all known sound algorithms learning a local region of the network require an exponential number of training instances to the size of the learned region . The contribution of this paper is two-fold . We introduce a novel local algorithm that returns all variables with direct edges to and from a target variable T as well as a local algorithm that returns the Markov Blanket of T. Both algorithms ( i ) are sound , ( ii ) can be run efficiently in datasets with thousands of variables , and ( iii ) significantly outperform in terms of approximating the true neighborhood previous state-of-the-art algorithms using only a fraction of the training size required by the existing methods . A fundamental difference between our approach and existing ones is that the required sample depends on the generating graph connectivity and not the size of the local region ; this yields up to exponential savings in sample relative to previously known algorithms . The results presented here are promising not only for discovery of local causal structure , and variable selection for classification , but also for the induction of complete BNs .

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

bayesian networks
novel data mining algorithms
robust and scalable statistical methods


Named entity mining from click-through data using weakly supervised latent dirichlet allocation This paper addresses Named Entity Mining ( NEM ) , in which we mine knowledge about named entities such as movies , games , and books from a huge amount of data . NEM is potentially useful in many applications including web search , online advertisement , and recommender system . There are three challenges for the task : finding suitable data source , coping with the ambiguities of named entity classes , and incorporating necessary human supervision into the mining process . This paper proposes conducting NEM by using click-through data collected at a web search engine , employing a topic model that generates the click-through data , and learning the topic model by weak supervision from humans . Specifically , it characterizes each named entity by its associated queries and URLs in the click-through data . It uses the topic model to resolve ambiguities of named entity classes by representing the classes as topics . It employs a method , referred to as Weakly Supervised Latent Dirichlet Allocation ( WS-LDA ) , to accurately learn the topic model with partially labeled named entities . Experiments on a large scale click-through data containing over 1.5 billion query-URL pairs show that the proposed approach can conduct very accurate NEM and significantly outperforms the baseline .

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

named entity recognition
search log mining
web mining


Collective annotation of Wikipedia entities in web text To take the first step beyond keyword-based search toward entity-based search , suitable token spans ( `` spots '' ) on documents must be identified as references to real-world entities from an entity catalog . Several systems have been proposed to link spots on Web pages to entities in Wikipedia . They are largely based on local compatibility between the text around the spot and textual metadata associated with the entity . Two recent systems exploit inter-label dependencies , but in limited ways . We propose a general collective disambiguation approach . Our premise is that coherent documents refer to entities from one or a few related topics or domains . We give formulations for the trade-off between local spot-to-entity compatibility and measures of global coherence between entities . Optimizing the overall entity assignment is NP-hard . We investigate practical solutions based on local hill-climbing , rounding integer linear programs , and pre-clustering entities followed by local optimization within clusters . In experiments involving over a hundred manually-annotated Web pages and tens of thousands of spots , our approaches significantly outperform recently-proposed algorithms .

O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 inference
entity annotation/disambiguation
information search and retrieval


Learning spatially variant dissimilarity ( SVaD ) measures Clustering algorithms typically operate on a feature vector representation of the data and find clusters that are compact with respect to an assumed ( dis ) similarity measure between the data points in feature space . This makes the type of clusters identified highly dependent on the assumed similarity measure . Building on recent work in this area , we formally define a class of spatially varying dissimilarity measures and propose algorithms to learn the dissimilarity measure automatically from the data . The idea is to identify clusters that are compact with respect to the unknown spatially varying dissimilarity measure . Our experiments show that the proposed algorithms are more stable and achieve better accuracy on various textual data sets when compared with similar algorithms proposed in the literature .

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


Efficient anonymity-preserving data collection The output of a data mining algorithm is only as good as its inputs , and individuals are often unwilling to provide accurate data about sensitive topics such as medical history and personal finance . Individuals maybe willing to share their data , but only if they are assured that it will be used in an aggregate study and that it can not be linked back to them . Protocols for anonymity-preserving data collection provide this assurance , in the absence of trusted parties , by allowing a set of mutually distrustful respondents to anonymously contribute data to an untrusted data miner . To effectively provide anonymity , a data collection protocol must be collusion resistant , which means that even if all dishonest respondents collude with a dishonest data miner in an attempt to learn the associations between honest respondents and their responses , they will be unable to do so . To achieve collusion resistance , previously proposed protocols for anonymity-preserving data collection have quadratically many communication rounds in the number of respondents , and employ ( sometimes incorrectly ) complicated cryptographic techniques such as zero-knowledge proofs . We describe a new protocol for anonymity-preserving , collusion resistant data collection . Our protocol has linearly many communication rounds , and achieves collusion resistance without relying on zero-knowledge proofs . This makes it especially suitable for data mining scenarios with a large number of respondents .

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


Exploiting vulnerability to secure user privacy on a social networking site As ( one 's ) social network expands , a user 's privacy protection goes beyond his privacy settings and becomes a social networking problem . In this research , we aim to address some critical issues related to privacy protection : Would the highest privacy settings guarantee a secure protection ? Given the open nature of social networking sites , is it possible to manage one 's privacy protection ? With the diversity of one 's social media friends , how can one figure out an effective approach to balance between vulnerability and privacy ? We present a novel way to define a vulnerable friend from an individual user 's perspective is dependent on whether or not the user 's friends ' privacy settings protect the friend and the individual 's network of friends ( which includes the user ) . As a single vulnerable friend in a user 's social network might place all friends at risk , we resort to experiments and observe how much security an individual user can improve by unfriending a vulnerable friend . We also show how privacy weakens if newly accepted friends are unguarded or unprotected . This work provides a large-scale evaluation of new security and privacy indexes using a Facebook dataset . We present and discuss a new perspective for reasoning about social networking security . When a user accepts a new friend , the user should ensure that the new friend is not an increased security risk with the potential of negatively impacting the entire friend network . Additionally , by leveraging the indexes proposed and employing new strategies for unfriending vulnerable friends , it is possible to further improve security and privacy without changing the social networking site 's existing architecture .

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


Beyond blacklists : learning to detect malicious web sites from suspicious URLs Malicious Web sites are a cornerstone of Internet criminal activities . As a result , there has been broad interest in developing systems to prevent the end user from visiting such sites . In this paper , we describe an approach to this problem based on automated URL classification , using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs . These methods are able to learn highly predictive models by extracting and automatically analyzing tens of thousands of features potentially indicative of suspicious URLs . The resulting classifiers obtain 95-99 % accuracy , detecting large numbers of malicious Web sites from their URLs , with only modest false positives .

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

abuse and crime involving computers
l1-regularization
security and protection
supervised learning


Mining social networks for personalized email prioritization Email is one of the most prevalent communication tools today , and solving the email overload problem is pressingly urgent . A good way to alleviate email overload is to automatically prioritize received messages according to the priorities of each user . However , research on statistical learning methods for fully personalized email prioritization ( PEP ) has been sparse due to privacy issues , since people are reluctant to share personal messages and importance judgments with the research community . It is therefore important to develop and evaluate PEP methods under the assumption that only limited training examples can be available , and that the system can only have the personal email data of each user during the training and testing of the model for that user . This paper presents the first study ( to the best of our knowledge ) under such an assumption . Specifically , we focus on analysis of personal social networks to capture user groups and to obtain rich features that represent the social roles from the viewpoint of a particular user . We also developed a novel semi-supervised ( transductive ) learning algorithm that propagates importance labels from training examples to test examples through message and user nodes in a personal email network . These methods together enable us to obtain an enriched vector representation of each new email message , which consists of both standard features of an email message ( such as words in the title or body , sender and receiver IDs , etc. ) and the induced social features from the sender and receivers of the message . Using the enriched vector representation as the input in SVM classifiers to predict the importance level for each test message , we obtained significant performance improvement over the baseline system ( without induced social features ) in our experiments on a multi-user data collection . We obtained significant performance improvement over the baseline system ( without induced social features ) in our experiments on a multi-user data collection : the relative error reduction in MAE was 31 % in micro-averaging , and 14 % in macro-averaging .

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

applications
clustering
miscellaneous
social network
text mining


Data mining with sparse grids using simplicial basis functions Recently we presented a new approach ( 18 ) to the classification problem arising in data mining . It is based on the regularization network approach but , in contrast to other methods which employ ansatz functions associated to data points , we use a grid in the usually high-dimensional feature space for the minimization process . To cope with the curse of dimensionality , we employ sparse grids ( 49 ) . Thus , only O ( hn-1nd-1 ) instead of O ( hn-d ) grid points and unknowns are involved . Here d denotes the dimension of the feature space and hn = 2-n gives the mesh size . We use the sparse grid combination technique ( 28 ) where the classification problem is discretized and solved on a sequence of conventional grids with uniform mesh sizes in each dimension . The sparse grid solution is then obtained by linear combination . In contrast to our former work , where d-linear functions were used , we now apply linear basis functions based on a simplicial discretization . This allows to handle more dimensions and the algorithm needs less operations per data point . We describe the sparse grid combination technique for the classification problem , give implementational details and discuss the complexity of the algorithm . It turns out that the method scales linearly with the number of given data points . Finally we report on the quality of the classifier built by our new method on data sets with up to 10 dimensions . It turns out that our new method achieves correctness rates which are competitive to that of the best existing methods .

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

classification
combination technique
simplicial discretization
sparse grids


Efficient kernel feature extraction for massive data sets Maximum margin discriminant analysis ( MMDA ) was proposed that uses the margin idea for feature extraction . It often outperforms traditional methods like kernel principal component analysis ( KPCA ) and kernel Fisher discriminant analysis ( KFD ) . However , as in other kernel methods , its time complexity is cubic in the number of training points m , and is thus computationally inefficient on massive data sets . In this paper , we propose an ( 1 + ε ) 2-approximation algorithm for obtaining the MMDA features by extending the core vector machines . The resultant time complexity is only linear in m , while its space complexity is independent of m. Extensive comparisons with the original MMDA , KPCA , and KFD on a number of large data sets show that the proposed feature extractor can improve classification accuracy , and is also faster than these kernel-based methods by more than an order of magnitude .

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

design methodology
learning
scalability
svm


Mining images on semantics via statistical learning In this paper , we have proposed a novel framework to enable hierarchical image classification via statistical learning . By integrating the concept hierarchy for semantic image concept organization , a hierarchical mixture model is proposed to enable multi-level modeling of semantic image concepts and hierarchical classifier combination . Thus , learning the classifiers for the semantic image concepts at the high level of the concept hierarchy can be effectively achieved by detecting the presences of the relevant base-level atomic image concepts . To effectively learn the base-level classifiers for the atomic image concepts at the first level of the concept hierarchy , we have proposed a novel adaptive EM algorithm to achieve more effective model selection and parameter estimation . In addition , a novel penalty term is proposed to effectively eliminate the misleading effects of the outlying unlabeled images on semi-supervised classifier training . Our experimental results in a specific image domain of outdoor photos are very attractive .

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

adaptive em algorithm
hierarchical mixture model
image classification


A generative probabilistic approach to visualizing sets of symbolic sequences There is a notable interest in extending probabilistic generative modeling principles to accommodate for more complex structured data types . In this paper we develop a generative probabilistic model for visualizing sets of discrete symbolic sequences . The model , a constrained mixture of discrete hidden Markov models , is a generalization of density-based visualization methods previously developed for static data sets . We illustrate our approach on sequences representing web-log data and chorals by J.S. Bach .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 algorithm
hidden markov model
information search and retrieval
latent space models
topographic mapping


Similarity measure based on partial information of time series Similarity measure of time series is an important subroutine in many KDD applications . Previous similarity models mainly focus on the prominent series behaviors by considering the whole information of time series . In this paper , we address the problem : which portion of information is more suitable for similarity measure for the data collected from a certain field . We propose a model for the retrieval and representation of the partial information in time series data , and a methodology for evaluating the similarity measurements based on partial information . The methodology is to retrieve various portions of information from the raw data and represent it in a concise form , then cluster the time series using the partial information and evaluate the similarity measurements through comparing the results with a standard classification . Experiments on data set from stock market give some interesting observations and justify the usefulness of our approach .

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

partial information
similarity measure
time series


Privacy preserving mining of association rules We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transactions . While it is feasible to recover association rules and preserve privacy using a straightforward `` uniform '' randomization , the discovered rules can unfortunately be exploited to find privacy breaches . We analyze the nature of privacy breaches and propose a class of randomization operators that are much more effective than uniform randomization in limiting the breaches . We derive formulae for an unbiased support estimator and its variance , which allow us to recover itemset supports from randomized datasets , and show how to incorporate these formulae into mining algorithms . Finally , we present experimental results that validate the algorithm by applying it on real datasets .

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


COA : finding novel patents through text analysis In recent years , the number of patents filed by the business enterprises in the technology industry are growing rapidly , thus providing unprecedented opportunities for knowledge discovery in patent data . One important task in this regard is to employ data mining techniques to rank patents in terms of their potential to earn money through licensing . Availability of such ranking can substantially reduce enterprise IP ( Intellectual Property ) management costs . Unfortunately , the existing software systems in the IP domain do not address this task directly . Through our research , we build a patent ranking software , named COA ( Claim Originality Analysis ) that rates a patent based on its value by measuring the recency and the impact of the important phrases that appear in the `` claims '' section of a patent . Experiments show that COA produces meaningful ranking when comparing it with other indirect patent evaluation metrics -- citation count , patent status , and attorney 's rating . In reallife settings , this tool was used by beta-testers in the IBM IP department . Lawyers found it very useful in patent rating , specifically , in highlighting potentially valuable patents in a patent cluster . In this article , we describe the ranking techniques and system architecture of COA . We also present the results that validate its effectiveness .

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

document ranking
information retrieval
patent processing
patent visualization


Discovering the set of fundamental rule changes The world around us changes constantly . Knowing what has changed is an important part of our lives . For businesses , recognizing changes is also crucial . It allows businesses to adapt themselves to the changing market needs . In this paper , we study changes of association rules from one time period to another . One approach is to compare the supports and\/or confidences of each rule in the two time periods and report the differences . This technique , however , is too simplistic as it tends to report a huge number of rule changes , and many of them are , in fact , simply the snowball effect of a small subset of fundamental changes . Here , we present a technique to highlight the small subset of fundamental changes . A change is fundamental if it can not be explained by some other changes . The proposed technique has been applied to a number of real-life datasets . Experiments results show that the number of rules whose changes are unexplainable is quite small ( about 20 % of the total number of changes discovered ) , and many of these unexplainable changes reflect some fundamental shifts in the application domain .

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


Analyzing patterns of user content generation in online social networks Various online social networks ( OSNs ) have been developed rapidly on the Internet . Researchers have analyzed different properties of such OSNs , mainly focusing on the formation and evolution of the networks as well as the information propagation over the networks . In knowledge-sharing OSNs , such as blogs and question answering systems , issues on how users participate in the network and how users `` generate\/contribute '' knowledge are vital to the sustained and healthy growth of the networks . However , related discussions have not been reported in the research literature . In this work , we empirically study workloads from three popular knowledge-sharing OSNs , including a blog system , a social bookmark sharing network , and a question answering social network to examine these properties . Our analysis consistently shows that ( 1 ) users ' posting behavior in these networks exhibits strong daily and weekly patterns , but the user active time in these OSNs does not follow exponential distributions ; ( 2 ) the user posting behavior in these OSNs follows stretched exponential distributions instead of power-law distributions , indicating the influence of a small number of core users can not dominate the network ; ( 3 ) the distributions of user contributions on high-quality and effort-consuming contents in these OSNs have smaller stretch factors for the stretched exponential distribution . Our study provides insights into user activity patterns and lays out an analytical foundation for further understanding various properties of these OSNs .

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

social networks
stretched exponential distribution

user generated content
user/machine systems


Discovering word senses from text Inventories of manually compiled dictionaries usually serve as a source for word senses . However , they often include many rare senses while missing corpus\/domain-specific senses . We present a clustering algorithm called CBC ( Clustering By Committee ) that automatically discovers word senses from text . It initially discovers a set of tight clusters called committees that are well scattered in the similarity space . The centroid of the members of a committee is used as the feature vector of the cluster . We proceed by assigning words to their most similar clusters . After assigning an element to a cluster , we remove their overlapping features from the element . This allows CBC to discover the less frequent senses of a word and to avoid discovering duplicate senses . Each cluster that a word belongs to represents one of its senses . We also present an evaluation methodology for automatically measuring the precision and recall of discovered senses .

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


Automating exploratory data analysis for efficient data mining

attribute selection
automation
encoding
transformation


Structural and temporal analysis of the blogosphere through community factorization The blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals . In this paper , we propose a novel technique that captures the structure and temporal dynamics of blog communities . In our framework , a community is a set of blogs that communicate with each other triggered by some events ( such as a news article ) . The community is represented by its structure and temporal dynamics : a community graph indicates how often one blog communicates with another , and a community intensity indicates the activity level of the community that varies over time . Our method , community factorization , extracts such communities from the blogosphere , where the communication among blogs is observed as a set of subgraphs ( i.e. , threads of discussion ) . This community extraction is formulated as a factorization problem in the framework of constrained optimization , in which the objective is to best explain the observed interactions in the blogosphere over time . We further provide a scalable algorithm for computing solutions to the constrained optimization problems . Extensive experimental studies on both synthetic and real blog data demonstrate that our technique is able to discover meaningful communities that are not detectable by traditional methods .

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

blog
blogosphere
community factorization

iterative search
non-negative matrix factorization
regularization


A model for discovering customer value for E-content There exists a huge demand for multimedia goods and services in the Internet . Currently available bandwidth speeds can support sale of downloadable content like CDs , e-books , etc. as well as services like video-on-demand . In the future , such services will be prevalent in the Internet . Since costs are typically fixed , maximizing revenue can maximize profits . A primary determinant of revenue in such e-content markets is how much value the customers associate with the content . Though marketing surveys are useful , they can not adapt to the dynamic nature of the Internet market . In this work , we examine how to learn customer valuations in close to real-time . Our contributions in this paper are threefold : ( 1 ) we develop a probabilistic model to describe customer behavior , ( 2 ) we develop a framework for pricing e-content based on basic economic principles , and ( 3 ) we propose a price discovering algorithm that learns customer behavior parameters and suggests prices to an e-content provider . We validate our algorithm using simulations . Our simulations indicate that our algorithm generates revenue close to the maximum expectation . Further , they also indicate that the algorithm is robust to transient customer behavior .

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


The IGrid index : reversing the dimensionality curse for similarity indexing in high dimensional space


Feedback effects between similarity and social influence in online communities A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties . People are similar to their neighbors in a social network for two distinct reasons : first , they grow to resemble their current friends due to social influence ; and second , they tend to form new links to others who are already like them , a process often termed selection by sociologists . While both factors are present in everyday social processes , they are in tension : social influence can push systems toward uniformity of behavior , while selection can lead to fragmentation . As such , it is important to understand the relative effects of these forces , and this has been a challenge due to the difficulty of isolating and quantifying them in real settings . We develop techniques for identifying and modeling the interactions between social influence and selection , using data from online communities where both social interaction and changes in behavior over time can be measured . We find clear feedback effects between the two factors , with rising similarity between two individuals serving , in aggregate , as an indicator of future interaction -- but with similarity then continuing to increase steadily , although at a slower rate , for long periods after initial interactions . We also consider the relative value of similarity and social influence in modeling future behavior . For instance , to predict the activities that an individual is likely to do next , is it more useful to know the current activities of their friends , or of the people most similar to them ?

O O O O O B I 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 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 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 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

online communities
social influence
social networks


Time series shapelets : a new primitive for data mining Classification of time series has been attracting great interest over the past decade . Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems . While this may be considered good news , given the simplicity of implementing the nearest neighbor algorithm , there are some negative consequences of this . First , the nearest neighbor algorithm requires storing and searching the entire dataset , resulting in a time and space complexity that limits its applicability , especially on resource-limited sensors . Second , beyond mere classification accuracy , we often wish to gain some insight into the data . In this work we introduce a new time series primitive , time series shapelets , which addresses these limitations . Informally , shapelets are time series subsequences which are in some sense maximally representative of a class . As we shall show with extensive empirical evaluations in diverse domains , algorithms based on the time series shapelet primitives can be interpretable , more accurate and significantly faster than state-of-the-art classifiers .

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 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