Ensemble-index : a new approach to indexing large databases The problem of similarity search ( query-by-content ) has attracted much research interest . It is a difficult problem because of the inherently high dimensionality of the data . The most promising solutions involve performing dimensionality reduction on the data , then indexing the reduced data with a multidimensional index structure . Many dimensionality reduction techniques have been proposed , including Singular Value Decomposition ( SVD ) , the Discrete Fourier Transform ( DFT ) , the Discrete Wavelet Transform ( DWT ) and Piecewise Polynomial Approximation . In this work , we introduce a novel framework for using ensembles of two or more representations for more efficient indexing . The basic idea is that instead of committing to a single representation for an entire dataset , different representations are chosen for indexing different parts of the database . The representations are chosen based upon a local view of the database . For example , sections of the data that can achieve a high fidelity representation with wavelets are indexed as wavelets , but highly spectral sections of the data are indexed using the Fourier transform . At query time , it is necessary to search several small heterogeneous indices , rather than one large homogeneous index . As we will theoretically and empirically demonstrate this results in much faster query response times .

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 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

dimensionality reduction
similarity search

content analysis and indexing
data mining
indexing and retrieval
time series


A generalized maximum entropy approach to bregman co-clustering and matrix approximation Co-clustering is a powerful data mining technique with varied applications such as text clustering , microarray analysis and recommender systems . Recently , an information-theoretic co-clustering approach applicable to empirical joint probability distributions was proposed . In many situations , co-clustering of more general matrices is desired . In this paper , we present a substantially generalized co-clustering framework wherein any Bregman divergence can be used in the objective function , and various conditional expectation based constraints can be considered based on the statistics that need to be preserved . Analysis of the co-clustering problem leads to the minimum Bregman information principle , which generalizes the maximum entropy principle , and yields an elegant meta algorithm that is guaranteed to achieve local optimality . Our methodology yields new algorithms and also encompasses several previously known clustering and co-clustering algorithms based on alternate minimization .

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

bregman divergences
co-clustering
learning


Can we learn a template-independent wrapper for news article extraction from a single training site ? Automatic news extraction from news pages is important in many Web applications such as news aggregation . However , the existing news extraction methods based on template-level wrapper induction have three serious limitations . First , the existing methods can not correctly extract pages belonging to an unseen template . Second , it is costly to maintain up-to-date wrappers for a large amount of news websites , because any change of a template may invalidate the corresponding wrapper . Last , the existing methods can merely extract unformatted plain texts , and thus are not user friendly . In this paper , we tackle the problem of template-independent Web news extraction in a user-friendly way . We formalize Web news extraction as a machine learning problem and learn a template-independent wrapper using a very small number of labeled news pages from a single site . Novel features dedicated to news titles and bodies are developed . Correlations between news titles and news bodies are exploited . Our template-independent wrapper can extract news pages from different sites regardless of templates . Moreover , our approach can extract not only texts , but also images and animates within the news bodies and the extracted news articles are in the same visual style as in the original pages . In our experiments , a wrapper learned from 40 pages from a single news site achieved an accuracy of 98.1 % on 3,973 news pages from 12 news sites .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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
data extraction
miscellaneous
web mining


Trajectory clustering with mixtures of regression models


A general probabilistic framework for clustering individuals and objects


Identifying bridging rules between conceptual clusters A bridging rule in this paper has its antecedent and action from different conceptual clusters . We first design two algorithms for mining bridging rules between clusters in a database , and then propose two non-linear metrics for measuring the interestingness of bridging rules . Bridging rules can be distinct from association rules ( or frequent itemsets ) . This is because ( 1 ) bridging rules can be generated by infrequent itemsets that are pruned in association rule mining ; and ( 2 ) bridging rules are measured by the importance that includes the distance between two conceptual clusters , whereas frequent itemsets are measured by only the support .

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


Effective and efficient itemset pattern summarization : regression-based approaches In this paper , we propose a set of novel regression-based approaches to effectively and efficiently summarize frequent itemset patterns . Specifically , we show that the problem of minimizing the restoration error for a set of itemsets based on a probabilistic model corresponds to a non-linear regression problem . We show that under certain conditions , we can transform the nonlinear regression problem to a linear regression problem . We propose two new methods , k-regression and tree-regression , to partition the entire collection of frequent itemsets in order to minimize the restoration error . The K-regression approach , employing a K-means type clustering method , guarantees that the total restoration error achieves a local minimum . The tree-regression approach employs a decision-tree type of top-down partition process . In addition , we discuss alternatives to estimate the frequency for the collection of itemsets being covered by the k representative itemsets . The experimental evaluation on both real and synthetic datasets demonstrates that our approaches significantly improve the summarization performance in terms of both accuracy ( restoration error ) , and computational cost .

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


Active learning using adaptive resampling

classification
learning
machine learning


Knowledge-based data mining We describe techniques for combining two types of knowledge systems : expert and machine learning . Both the expert system and the learning system represent information by logical decision rules or trees . Unlike the classical views of knowledge-base evaluation or refinement , our view accepts the contents of the knowledge base as completely correct . The knowledge base and the results of its stored cases will provide direction for the discovery of new relationships in the form of newly induced decision rules . An expert system called SEAS was built to discover sales leads for computer products and solutions . The system interviews executives by asking questions , and based on the responses , recommends products that may improve a business ' operations . Leveraging this expert system , we record the results of the interviews and the program 's recommendations . The very same data stored by the expert system is used to find new predictive rules . Among the potential advantages of this approach are ( a ) the capability to spot new sales trends and ( b ) the substitution of less expensive probabilistic rules that use database data instead of interviews .

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

database applications
decision rule induction
expert systems


Learning to detect malicious executables in the wild In this paper , we describe the development of a fielded application for detecting malicious executables in the wild . We gathered 1971 benign and 1651 malicious executables and encoded each as a training example using n-grams of byte codes as features . Such processing resulted in more than 255 million distinct n-grams . After selecting the most relevant n-grams for prediction , we evaluated a variety of inductive methods , including naive Bayes , decision trees , support vector machines , and boosting . Ultimately , boosted decision trees outperformed other methods with an area under the roc curve of 0.996 . Results also suggest that our methodology will scale to larger collections of executables . To the best of our knowledge , ours is the only fielded application for this task developed using techniques from machine learning and data mining .

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


Mining optimal decision trees from itemset lattices We present DL8 , an exact algorithm for finding a decision tree that optimizes a ranking function under size , depth , accuracy and leaf constraints . Because the discovery of optimal trees has high theoretical complexity , until now few efforts have been made to compute such trees for real-world datasets . An exact algorithm is of both scientific and practical interest . From a scientific point of view , it can be used as a gold standard to evaluate the performance of heuristic constraint-based decision tree learners and to gain new insight in traditional decision tree learners . From the application point of view , it can be used to discover trees that can not be found by heuristic decision tree learners . The key idea behind our algorithm is that there is a relation between constraints on decision trees and constraints on itemsets . We show that optimal decision trees can be extracted from lattices of itemsets in linear time . We give several strategies to efficiently build these lattices . Experiments show that under the same constraints , DL8 obtains better results than C4 .5 , which confirms that exhaustive search does not always imply overfitting . The results also show that DL8 is a useful and interesting tool to learn decision trees under constraints .

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

constraint-based mining
formal concepts
frequent itemsets


IntelliClean : a knowledge-based intelligent data cleaner


Influence and correlation in social networks In many online social systems , social ties between users play an important role in dictating their behavior . One of the ways this can happen is through social influence , the phenomenon that the actions of a user can induce his\/her friends to behave in a similar way . In systems where social influence exists , ideas , modes of behavior , or new technologies can diffuse through the network like an epidemic . Therefore , identifying and understanding social influence is of tremendous interest from both analysis and design points of view . This is a difficult task in general , since there are factors such as homophily or unobserved confounding variables that can induce statistical correlation between the actions of friends in a social network . Distinguishing influence from these is essentially the problem of distinguishing correlation from causality , a notoriously hard statistical problem . In this paper we study this problem systematically . We define fairly general models that replicate the aforementioned sources of social correlation . We then propose two simple tests that can identify influence as a source of social correlation when the time series of user actions is available . We give a theoretical justification of one of the tests by proving that with high probability it succeeds in ruling out influence in a rather general model of social correlation . We also simulate our tests on a number of examples designed by randomly generating actions of nodes on a real social network ( from Flickr ) according to one of several models . Simulation results confirm that our test performs well on these data . Finally , we apply them to real tagging data on Flickr , exhibiting that while there is significant social correlation in tagging behavior on this system , this correlation can not be attributed to social influence .

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

correlation
social influence
social networks
tagging


Estimating rates of rare events at multiple resolutions We consider the problem of estimating occurrence rates of rare eventsfor extremely sparse data , using pre-existing hierarchies to perform inference at multiple resolutions . In particular , we focus on the problem of estimating click rates for ( webpage , advertisement ) pairs ( called impressions ) where both the pages and the ads are classified into hierarchies that capture broad contextual information at different levels of granularity . Typically the click rates are low and the coverage of the hierarchies is sparse . To overcome these difficulties we devise a sampling method whereby we analyze aspecially chosen sample of pages in the training set , and then estimate click rates using a two-stage model . The first stage imputes the number of ( webpage , ad ) pairs at all resolutions of the hierarchy to adjust for the sampling bias . The second stage estimates clickrates at all resolutions after incorporating correlations among sibling nodes through a tree-structured Markov model . Both models are scalable and suited to large scale data mining applications . On a real-world dataset consisting of 1\/2 billion impressions , we demonstrate that even with 95 % negative ( non-clicked ) events in the training set , our method can effectively discriminate extremely rare events in terms of their click propensity .

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

clickthrough rate
contextual matching
hypertext/hypermedia
imputation
internet advertising
maximum entropy
miscellaneous
on-line information services
tree-structured markov model


Efficient closed pattern mining in the presence of tough block constraints Various constrained frequent pattern mining problem formulations and associated algorithms have been developed that enable the user to specify various itemset-based constraints that better capture the underlying application requirements and characteristics . In this paper we introduce a new class of block constraints that determine the significance of an itemset pattern by considering the dense block that is formed by the pattern 's items and its associated set of transactions . Block constraints provide a natural framework by which a number of important problems can be specified and make it possible to solve numerous problems on binary and real-valued datasets . However , developing computationally efficient algorithms to find these block constraints poses a number of challenges as unlike the different itemset-based constraints studied earlier , these block constraints are tough as they are neither anti-monotone , monotone , nor convertible . To overcome this problem , we introduce a new class of pruning methods that significantly reduce the overall search space and present a computationally efficient and scalable algorithm called CBMiner to find the closed itemsets that satisfy the block constraints .

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


Topic-conditioned novelty detection Automated detection of the first document reporting each new event in temporally-sequenced streams of documents is an open challenge . In this paper we propose a new approach which addresses this problem in two stages : 1 ) using a supervised learning algorithm to classify the on-line document stream into pre-defined broad topic categories , and 2 ) performing topic-conditioned novelty detection for documents in each topic . We also focus on exploiting named-entities for event-level novelty detection and using feature-based heuristics derived from the topic histories . Evaluating these methods using a set of broadcast news stories , our results show substantial performance gains over the traditional one-level approach to the novelty detection problem .

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

design methodology
feature selection
information search and retrieval
named entity
text classification


Learning incoherent sparse and low-rank patterns from multiple tasks We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks . Our approach is based on a linear multi-task learning formulation , in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint , respectively . This formulation is non-convex ; we convert it into its convex surrogate , which can be routinely solved via semidefinite programming for small-size problems . We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate ; however , in the optimization formulation , the objective function is non-differentiable and the feasible domain is non-trivial . We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme . The computation of projected gradient involves a constrained optimization problem ; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem . In addition , we present two projected gradient algorithms and discuss their rates of convergence . Experimental results on benchmark data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

multi-task learning
sparse and low-rank patterns
trace norm


Privacy preserving regression modelling via distributed computation Reluctance of data owners to share their possibly confidential or proprietary data with others who own related databases is a serious impediment to conducting a mutually beneficial data mining analysis . We address the case of vertically partitioned data -- multiple data owners\/agencies each possess a few attributes of every data record . We focus on the case of the agencies wanting to conduct a linear regression analysis with complete records without disclosing values of their own attributes . This paper describes an algorithm that enables such agencies to compute the exact regression coefficients of the global regression equation and also perform some basic goodness-of-fit diagnostics while protecting the confidentiality of their data . In more general settings beyond the privacy scenario , this algorithm can also be viewed as method for the distributed computation for regression analyses .

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

data confidentiality
data integration
learning
secure multi-party computation


Large-scale sparse logistic regression Logistic Regression is a well-known classification method that has been used widely in many applications of data mining , machine learning , computer vision , and bioinformatics . Sparse logistic regression embeds feature selection in the classification framework using the l1-norm regularization , and is attractive in many applications involving high-dimensional data . In this paper , we propose Lassplore for solving large-scale sparse logistic regression . Specifically , we formulate the problem as the l1-ball constrained smooth convex optimization , and propose to solve the problem using the Nesterov 's method , an optimal first-order black-box method for smooth convex optimization . One of the critical issues in the use of the Nesterov 's method is the estimation of the step size at each of the optimization iterations . Previous approaches either applies the constant step size which assumes that the Lipschitz gradient is known in advance , or requires a sequence of decreasing step size which leads to slow convergence in practice . In this paper , we propose an adaptive line search scheme which allows to tune the step size adaptively and meanwhile guarantees the optimal convergence rate . Empirical comparisons with several state-of-the-art algorithms demonstrate the efficiency of the proposed Lassplore algorithm for large-scale problems .

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

adaptive line search
logistic regression

l1-ball constraint
nesterov's method
sparse learning


Out-of-core frequent pattern mining on a commodity PC In this work we focus on the problem of frequent itemset mining on large , out-of-core data sets . After presenting a characterization of existing out-of-core frequent itemset mining algorithms and their drawbacks , we introduce our efficient , highly scalable solution . Presented in the context of the FPGrowth algorithm , our technique involves several novel I\/O-conscious optimizations , such as approximate hash-based sorting and blocking , and leverages recent architectural advancements in commodity computers , such as 64-bit processing . We evaluate the proposed optimizations on truly large data sets , up to 75GB , and show they yield greater than a 400-fold execution time improvement . Finally , we discuss the impact of this research in the context of other pattern mining challenges , such as sequence mining and graph mining .

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


Information-theoretic co-clustering Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text , web-log and market-basket data analysis . A basic problem in contingency table analysis is co-clustering : simultaneous clustering of the rows and columns . A novel theoretical formulation views the contingency table as an empirical joint probability distribution of two discrete random variables and poses the co-clustering problem as an optimization problem in information theory -- the optimal co-clustering maximizes the mutual information between the clustered random variables subject to constraints on the number of row and column clusters . We present an innovative co-clustering algorithm that monotonically increases the preserved mutual information by intertwining both the row and column clusterings at all stages . Using the practical example of simultaneous word-document clustering , we demonstrate that our algorithm works well in practice , especially in the presence of sparsity and high-dimensionality .

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

clustering
information theory
mutual information

co-clustering
information search and retrieval


Clustering moving objects Due to the advances in positioning technologies , the real time information of moving objects becomes increasingly available , which has posed new challenges to the database research . As a long-standing technique to identify overall distribution patterns in data , clustering has achieved brilliant successes in analyzing static datasets . In this paper , we study the problem of clustering moving objects , which could catch interesting pattern changes during the motion process and provide better insight into the essence of the mobile data points . In order to catch the spatial-temporal regularities of moving objects and handle large amounts of data , micro-clustering ( 20 ) is employed . Efficient techniques are proposed to keep the moving micro-clusters geographically small . Important events such as the collisions among moving micro-clusters are also identified . In this way , high quality moving micro-clusters are dynamically maintained , which leads to fast and competitive clustering result at any given time instance . We validate our approaches with a through experimental evaluation , where orders of magnitude improvement on running time is observed over normal K-Means clustering method ( 14 ) .

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


Algorithms for estimating relative importance in networks Large and complex graphs representing relationships among sets of entities are an increasingly common focus of interest in data analysis -- examples include social networks , Web graphs , telecommunication networks , and biological networks . In interactive analysis of such data a natural query is `` which entities are most important in the network relative to a particular individual or set of individuals ? '' We investigate the problem of answering such queries in this paper , focusing in particular on defining and computing the importance of nodes in a graph relative to one or more root nodes . We define a general framework and a number of different algorithms , building on ideas from social networks , graph theory , Markov models , and Web graph analysis . We experimentally evaluate the different properties of these algorithms on toy graphs and demonstrate how our approach can be used to study relative importance in real-world networks including a network of interactions among September 11th terrorists , a network of collaborative research in biotechnology among companies and universities , and a network of co-authorship relationships among computer science researchers .

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 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 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 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

graphs
relative importance
social networks

information search and retrieval
markov chains
pagerank


Finding surprising patterns in a time series database in linear time and space The problem of finding a specified pattern in a time series database ( i.e. query by content ) has received much attention and is now a relatively mature field . In contrast , the important problem of enumerating all surprising or interesting patterns has received far less attention . This problem requires a meaningful definition of `` surprise '' , and an efficient search technique . All previous attempts at finding surprising patterns in time series use a very limited notion of surprise , and\/or do not scale to massive datasets . To overcome these limitations we introduce a novel technique that defines a pattern surprising if the frequency of its occurrence differs substantially from that expected by chance , given some previously seen data .

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anomaly detection
feature extraction
markov model
novelty detection
suffix tree


Local sparsity control for naive Bayes with extreme misclassification costs In applications of data mining characterized by highly skewed misclassification costs certain types of errors become virtually unacceptable . This limits the utility of a classifier to a range in which such constraints can be met . Naive Bayes , which has proven to be very useful in text mining applications due to high scalability , can be particularly affected . Although its 0\/1 loss tends to be small , its misclassifications are often made with apparently high confidence . Aside from efforts to better calibrate Naive Bayes scores , it has been shown that its accuracy depends on document sparsity and feature selection can lead to marked improvement in classification performance . Traditionally , sparsity is controlled globally , and the result for any particular document may vary . In this work we examine the merits of local sparsity control for Naive Bayes in the context of highly asymmetric misclassification costs . In experiments with three benchmark document collections we demonstrate clear advantages of document-level feature selection . In the extreme cost setting , multinomial Naive Bayes with local sparsity control is able to outperform even some of the recently proposed effective improvements to the Naive Bayes classifier . There are also indications that local feature selection may be preferable in different cost settings .

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

high recall classification
text categorization