Composition attacks and auxiliary information in data privacy Privacy is an increasingly important aspect of data publishing . Reasoning about privacy , however , is fraught with pitfalls . One of the most significant is the auxiliary information ( also called external knowledge , background knowledge , or side information ) that an adversary gleans from other channels such as the web , public records , or domain knowledge . This paper explores how one can reason about privacy in the face of rich , realistic sources of auxiliary information . Specifically , we investigate the effectiveness of current anonymization schemes in preserving privacy when multiple organizations independently release anonymized data about overlapping populations . 1 . We investigate composition attacks , in which an adversary uses independent anonymized releases to breach privacy . We explain why recently proposed models of limited auxiliary information fail to capture composition attacks . Our experiments demonstrate that even a simple instance of a composition attack can breach privacy in practice , for a large class of currently proposed techniques . The class includes k-anonymity and several recent variants . 2 . On a more positive note , certain randomization-based notions of privacy ( such as differential privacy ) provably resist composition attacks and , in fact , the use of arbitrary side information . This resistance enables `` stand-alone '' design of anonymization schemes , without the need for explicitly keeping track of other releases . We provide a precise formulation of this property , and prove that an important class of relaxations of differential privacy also satisfy the property . This significantly enlarges the class of protocols known to enable modular design .

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


Learning , indexing , and diagnosing network faults Modern communication networks generate massive volume of operational event data , e.g. , alarm , alert , and metrics , which can be used by a network management system ( NMS ) to diagnose potential faults . In this work , we introduce a new class of indexable fault signatures that encode temporal evolution of events generated by a network fault as well as topological relationships among the nodes where these events occur . We present an efficient learning algorithm to extract such fault signatures from noisy historical event data , and with the help of novel space-time indexing structures , we show how to perform efficient , online signature matching . We provide results from extensive experimental studies to explore the efficacy of our approach and point out potential applications of such signatures for many different types of networks including social and information networks .

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

fault signature
network topology
online diagnosis


SVM selective sampling for ranking with application to data retrieval Learning ranking ( or preference ) functions has been a major issue in the machine learning community and has produced many applications in information retrieval . SVMs ( Support Vector Machines ) - a classification and regression methodology - have also shown excellent performance in learning ranking functions . They effectively learn ranking functions of high generalization based on the `` large-margin '' principle and also systematically support nonlinear ranking by the `` kernel trick '' . In this paper , we propose an SVM selective sampling technique for learning ranking functions . SVM selective sampling ( or active learning with SVM ) has been studied in the context of classification . Such techniques reduce the labeling effort in learning classification functions by selecting only the most informative samples to be labeled . However , they are not extendable to learning ranking functions , as the labeled data in ranking is relative ordering , or partial orders of data . Our proposed sampling technique effectively learns an accurate SVM ranking function with fewer partial orders . We apply our sampling technique to the data retrieval application , which enables fuzzy search on relational databases by interacting with users for learning their preferences . Experimental results show a significant reduction of the labeling effort in inducing accurate ranking functions .

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

active learning
ranking
selective sampling


Consensus

group

stable

feature

selection

Stability

is

an

important

yet

under-addressed

issue

in

feature

selection

from

high-dimensional

and

small

sample

data

In

this

paper

we

show

that

stability

of

feature

selection

has

a

strong

dependency

on

sample

size

We

propose

a

novel

framework

for

stable

feature

selection

which

first

identifies

consensus

feature

groups

from

subsampling

of

training

samples

and

then

performs

feature

selection

by

treating

each

consensus

feature

group

as

a

single

entity

Experiments

on

both

synthetic

and

real-world

data

sets

show

that

an

algorithm

developed

under

this

framework

is

effective

at

alleviating

the

problem

of

small

sample

size

and

leads

to

more

stable

feature

selection

results

and

comparable

or

better

generalization

performance

than

state-of-the-art

feature

selection

algorithms

Synthetic

data

sets

and

algorithm

source

code

are

available

at

http:\/\/www.cs.binghamton.edu\/~lyu\/KDD09\/

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

feature selection
small sample
stability


Clinical and financial outcomes analysis with existing hospital patient records Existing patient records are a valuable resource for automated outcomes analysis and knowledge discovery . However , key clinical data in these records is typically recorded in unstructured form as free text and images , and most structured clinical information is poorly organized . Time-consuming interpretation and analysis is required to convert these records into structured clinical data . Thus , only a tiny fraction of this resource is utilized . We present REMIND , a Bayesian Framework for Reliable Extraction and Meaningful Inference from Nonstructured Data . REMIND integrates and blends the structured and unstructured clinical data in patient records to automatically created high-quality structured clinical data . This structuring allows existing patient records to be mined for quality assurance , regulatory compliance , and to relate financial and clinical factors . We demonstrate REMIND on two medical applications : ( a ) Extract `` recurrence '' , the key outcome for measuring treatment effectiveness , for colon cancer patients ( ii ) Extract key diagnoses and complications for acute myocardial infarction ( heart attack ) patients , and demonstrate the impact of these clinical factors on financial outcomes .

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

bayes nets
data mining
database applications
hmms
temporal reasoning


Query result clustering for object-level search Query result clustering has recently attracted a lot of attention to provide users with a succinct overview of relevant results . However , little work has been done on organizing the query results for object-level search . Object-level search result clustering is challenging because we need to support diverse similarity notions over object-specific features ( such as the price and weight of a product ) of heterogeneous domains . To address this challenge , we propose a hybrid subspace clustering algorithm called Hydra . Algorithm Hydra captures the user perception of diverse similarity notions from millions of Web pages and disambiguates different senses using feature-based subspace locality measures . Our proposed solution , by combining wisdom of crowds and wisdom of data , achieves robustness and efficiency over existing approaches . We extensively evaluate our proposed framework and demonstrate how to enrich user experiences in object-level search using a real-world product search scenarios .

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

object-level search
performance evaluation


Model compression Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers . Unfortunately , the space required to store this many classifiers , and the time required to execute them at run-time , prohibits their use in applications where test sets are large ( e.g. Google ) , where storage space is at a premium ( e.g. PDAs ) , and where computational power is limited ( e.g. hea-ring aids ) . We present a method for `` compressing '' large , complex ensembles into smaller , faster models , usually without significant loss in performance .

B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 viewpoint patterns in image databases The increasing number of image repositories has made image mining an important task because of its potential in discovering useful image patterns from a large set of images . In this paper , we introduce the notion of viewpoint patterns for image databases . Viewpoint patterns refer to patterns that capture the invariant relationships of one object from the point of view of another object . These patterns are unique and significant in images because the absolute positional information of objects for most images is not important , but rather , it is the relative distance and orientation of the objects from each other that is meaningful . We design a scalable and efficient algorithm to discover such viewpoint patterns . Experiments results on various image sets demonstrate that viewpoint patterns are meaningful and interesting to human users .

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

database applications
image database
spatial relationship


IMMC : incremental maximum margin criterion Subspace learning approaches have attracted much attention in academia recently . However , the classical batch algorithms no longer satisfy the applications on streaming data or large-scale data . To meet this desirability , Incremental Principal Component Analysis ( IPCA ) algorithm has been well established , but it is an unsupervised subspace learning approach and is not optimal for general classification tasks , such as face recognition and Web document categorization . In this paper , we propose an incremental supervised subspace learning algorithm , called Incremental Maximum Margin Criterion ( IMMC ) , to infer an adaptive subspace by optimizing the Maximum Margin Criterion . We also present the proof for convergence of the proposed algorithm . Experimental results on both synthetic dataset and real world datasets show that IMMC converges to the similar subspace as that of batch approach .

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

learning
maximum margin criterion
principal component analysis

linear discriminant analysis
optimization


Microscopic evolution of social networks We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals . For the first time at such a large scale , we study individual node arrival and edge creation processes that collectively lead to macroscopic properties of networks . Using a methodology based on the maximum-likelihood principle , we investigate a wide variety of network formation strategies , and show that edge locality plays a critical role in evolution of networks . Our findings supplement earlier network models based on the inherently non-local preferential attachment . Based on our observations , we develop a complete model of network evolution , where nodes arrive at a prespecified rate and select their lifetimes . Each node then independently initiates edges according to a `` gap '' process , selecting a destination for each edge according to a simple triangle-closing model free of any parameters . We show analytically that the combination of the gap distribution with the node lifetime leads to a power law out-degree distribution that accurately reflects the true network in all four cases . Finally , we give model parameter settings that allow automatic evolution and generation of realistic synthetic networks of arbitrary scale .

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

graph generators
maximum likelihood
transitivity
triadic closure


Model order selection for boolean matrix factorization Matrix factorizations -- where a given data matrix is approximated by a product of two or more factor matrices -- are powerful data mining tools . Among other tasks , matrix factorizations are often used to separate global structure from noise . This , however , requires solving the ` model order selection problem ' of determining where fine-grained structure stops , and noise starts , i.e. , what is the proper size of the factor matrices . Boolean matrix factorization ( BMF ) -- where data , factors , and matrix product are Boolean -- has received increased attention from the data mining community in recent years . The technique has desirable properties , such as high interpretability and natural sparsity . But so far no method for selecting the correct model order for BMF has been available . In this paper we propose to use the Minimum Description Length ( MDL ) principle for this task . Besides solving the problem , this well-founded approach has numerous benefits , e.g. , it is automatic , does not require a likelihood function , is fast , and , as experiments show , is highly accurate . We formulate the description length function for BMF in general -- making it applicable for any BMF algorithm . We extend an existing algorithm for BMF to use MDL to identify the best Boolean matrix factorization , analyze the complexity of the problem , and perform an extensive experimental evaluation to study its behavior .

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

matrix factorizations
model order selection

boolean matrix factorizations
matrix decompositions
minimum description length principle
model selection


Friendship and mobility : user movement in location-based social networks Even though human movement and mobility patterns have a high degree of freedom and variation , they also exhibit structural patterns due to geographic and social constraints . Using cell phone location data , as well as data from two online location-based social networks , we aim to understand what basic laws govern human motion and dynamics . We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks . Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure , while long-distance travel is more influenced by social network ties . We show that social relationships can explain about 10 % to 30 % of all human movement , while periodic behavior explains 50 % to 70 % . Based on our findings , we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure . We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility .

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


Entity categorization over large document collections Extracting entities ( such as people , movies ) from documents and identifying the categories ( such as painter , writer ) they belong to enable structured querying and data analysis over unstructured document collections . In this paper , we focus on the problem of categorizing extracted entities . Most prior approaches developed for this task only analyzed the local document context within which entities occur . In this paper , we significantly improve the accuracy of entity categorization by ( i ) considering an entity 's context across multiple documents containing it , and ( ii ) exploiting existing large lists of related entities ( e.g. , lists of actors , directors , books ) . These approaches introduce computational challenges because ( a ) the context of entities has to be aggregated across several documents and ( b ) the lists of related entities may be very large . We develop techniques to address these challenges . We present a thorough experimental study on real data sets that demonstrates the increase in accuracy and the scalability of our approaches .

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


Combining partitions by probabilistic label aggregation Data clustering represents an important tool in exploratory data analysis . The lack of objective criteria render model selection as well as the identification of robust solutions particularly difficult . The use of a stability assessment and the combination of multiple clustering solutions represents an important ingredient to achieve the goal of finding useful partitions . In this work , we propose a novel way of combining multiple clustering solutions for both , hard and soft partitions : the approach is based on modeling the probability that two objects are grouped together . An efficient EM optimization strategy is employed in order to estimate the model parameters . Our proposal can also be extended in order to emphasize the signal more strongly by weighting individual base clustering solutions according to their consistency with the prediction for previously unseen objects . In addition to that , the probabilistic model supports an out-of-sample extension that ( i ) makes it possible to assign previously unseen objects to classes of the combined solution and ( ii ) renders the efficient aggregation of solutions possible . In this work , we also shed some light on the usefulness of such combination approaches . In the experimental result section , we demonstrate the competitive performance of our proposal in comparison with other recently proposed methods for combining multiple classifications of a finite data set .

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 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 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

consensus partition
learning
re-sampling


Group formation in large social networks : membership , growth , and evolution The processes by which communities come together , attract new members , and develop over time is a central research issue in the social sciences - political movements , professional organizations , and religious denominations all provide fundamental examples of such communities . In the digital domain , on-line groups are becoming increasingly prominent due to the growth of community and social networking sites such as MySpace and LiveJournal . However , the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities has left most basic questions about the evolution of such groups largely unresolved : what are the structural features that influence whether individuals will join communities , which communities will grow rapidly , and how do the overlaps among pairs of communities change over time . Here we address these questions using two large sources of data : friendship links and community membership on LiveJournal , and co-authorship and conference publications in DBLP . Both of these datasets provide explicit user-defined communities , where conferences serve as proxies for communities in DBLP . We study how the evolution of these communities relates to properties such as the structure of the underlying social networks . We find that the propensity of individuals to join communities , and of communities to grow rapidly , depends in subtle ways on the underlying network structure . For example , the tendency of an individual to join a community is influenced not just by the number of friends he or she has within the community , but also crucially by how those friends are connected to one another . We use decision-tree techniques to identify the most significant structural determinants of these properties . We also develop a novel methodology for measuring movement of individuals between communities , and show how such movements are closely aligned with changes in the topics of interest within the communities .

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

diffusion of innovations
on-line communities


Discrimination-aware data mining In the context of civil rights law , discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority , without regard to individual merit . Rules extracted from databases by data mining techniques , such as classification or association rules , when used for decision tasks such as benefit or credit approval , can be discriminatory in the above sense . In this paper , the notion of discriminatory classification rules is introduced and studied . Providing a guarantee of non-discrimination is shown to be a non trivial task . A naive approach , like taking away all discriminatory attributes , is shown to be not enough when other background knowledge is available . Our approach leads to a precise formulation of the redlining problem along with a formal result relating discriminatory rules with apparently safe ones by means of background knowledge . An empirical assessment of the results on the German credit dataset is also provided .

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


Clustering with relative constraints Recent studies have suggested using relative distance comparisons as constraints to represent domain knowledge . A natural extension to relative comparisons is the combination of two comparisons defined on the same set of three instances . Constraints in this form , termed Relative Constraints , provide a unified knowledge representation for both partitional and hierarchical clusterings . But many key properties of relative constraints remain unknown . In this paper , we answer the following important questions that enable the broader application of relative constraints in general clustering problems : '' Feasibility : Does there exist a clustering that satisfies a given set of relative constraints ? ( consistency of constraints ) `` Completeness : Given a set of consistent relative constraints , how can one derive a complete clustering without running into dead-ends ? '' Informativeness : How can one extract the most informative relative constraints from given knowledge sources ? We show that any hierarchical domain knowledge can be easily represented by relative constraints . We further present a hierarchical algorithm that finds a clustering satisfying all given constraints in polynomial time . Experiments showed that our algorithm achieves significantly higher accuracy than the existing metric learning approach based on relative comparisons .

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

constrained clustering
hierarchical clustering


Customer lifetime value modeling and its use for customer retention planning We present and discuss the important business problem of estimating the effect of retention efforts on the Lifetime Value of a customer in the Telecommunications industry . We discuss the components of this problem , in particular customer value and length of service ( or tenure ) modeling , and present a novel segment-based approach , motivated by the segment-level view marketing analysts usually employ . We then describe how we build on this approach to estimate the effects of retention on Lifetime Value . Our solution has been successfully implemented in Amdocs ' Business Insight ( BI ) platform , and we illustrate its usefulness in real-world scenarios .

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

churn modeling
incentive allocation
number-theoretic computations
retention campaign


Social action tracking via noise tolerant time-varying factor graphs It is well known that users ' behaviors ( actions ) in a social network are influenced by various factors such as personal interests , social influence , and global trends . However , few publications systematically study how social actions evolve in a dynamic social network and to what extent different factors affect the user actions . In this paper , we propose a Noise Tolerant Time-varying Factor Graph Model ( NTT-FGM ) for modeling and predicting social actions . NTT-FGM simultaneously models social network structure , user attributes and user action history for better prediction of the users ' future actions . More specifically , a user 's action at time t is generated by her latent state at t , which is influenced by her attributes , her own latent state at time t-1 and her neighbors ' states at time t and t-1 . Based on this intuition , we formalize the social action tracking problem using the NTT-FGM model ; then present an efficient algorithm to learn the model , by combining the ideas from both continuous linear system and Markov random field . Finally , we present a case study of our model on predicting future social actions . We validate the model on three different types of real-world data sets . Qualitatively , our model can uncover some interesting patterns of the social dynamics . Quantitatively , experimental results show that the proposed method outperforms several baseline methods for action prediction .

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 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

social influence analysis
time-varying factor graphs


Identifying prospective customers


Expertise modeling for matching papers with reviewers An essential part of an expert-finding task , such as matching reviewers to submitted papers , is the ability to model the expertise of a person based on documents . We evaluate several measures of the association between an author in an existing collection of research papers and a previously unseen document . We compare two language model based approaches with a novel topic model , Author-Persona-Topic ( APT ) . In this model , each author can write under one or more `` personas , '' which are represented as independent distributions over hidden topics . Examples of previous papers written by prospective reviewers are gathered from the Rexa database , which extracts and disambiguates author mentions from documents gathered from the web . We evaluate the models using a reviewer matching task based on human relevance judgments determining how well the expertise of proposed reviewers matches a submission . We find that the APT topic model outperforms the other models .

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


Segmentation-based modeling for advanced targeted marketing Fingerhut Business Intelligence ( BI ) has a long and successful history of building statistical models to predict consumer behavior . The models constructed are typically segmentation-based models in which the target audience is split into subpopulations ( i.e. , customer segments ) and individually tailored statistical models are then developed for each segment . Such models are commonly employed in the direct-mail industry ; however , segmentation is often performed on an ad-hoc basis without directly considering how segmentation affects the accuracy of the resulting segment models . Fingerhut BI approached IBM Research with the problem of how to build segmentation-based models more effectively so as to maximize predictive accuracy . The IBM Advanced Targeted Marketing-Single EventsTM ( IBM ATM-SETM ) solution is the result of IBM Research and Fingerhut BI directing their efforts jointly towards solving this problem . This paper presents an evaluation of ATM-SE 's modeling capabilities using data from Fingerhut 's catalog mailings .

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

database applications
decision trees
feature selection
linear regression
logistic regression
segmentation-based models


Towards an effective cooperation of the user and the computer for classification


Pragmatic text mining : minimizing human effort to quantify many issues in call logs We discuss our experiences in analyzing customer-support issues from the unstructured free-text fields of technical-support call logs . The identification of frequent issues and their accurate quantification is essential in order to track aggregate costs broken down by issue type , to appropriately target engineering resources , and to provide the best diagnosis , support and documentation for most common issues . We present a new set of techniques for doing this efficiently on an industrial scale , without requiring manual coding of calls in the call center . Our approach involves ( 1 ) a new text clustering method to identify common and emerging issues ; ( 2 ) a method to rapidly train large numbers of categorizers in a practical , interactive manner ; and ( 3 ) a method to accurately quantify categories , even in the face of inaccurate classifications and training sets that necessarily can not match the class distribution of each new month 's data . We present our methodology and a tool we developed and deployed that uses these methods for tracking ongoing support issues and discovering emerging issues at HP .

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

applications
decision support
log processing
supervised machine learning
text classification


Depth first generation of long patterns


Fast discovery of unexpected patterns in data , relative to a Bayesian network We consider a model in which background knowledge on a given domain of interest is available in terms of a Bayesian network , in addition to a large database . The mining problem is to discover unexpected patterns : our goal is to find the strongest discrepancies between network and database . This problem is intrinsically difficult because it requires inference in a Bayesian network and processing the entire , potentially very large , database . A sampling-based method that we introduce is efficient and yet provably finds the approximately most interesting unexpected patterns . We give a rigorous proof of the method 's correctness . Experiments shed light on its efficiency and practicality for large-scale Bayesian networks and databases .

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


Statistical modeling of large-scale simulation data With the advent of fast computer systems , scientists are now able to generate terabytes of simulation data . Unfortunately , the sheer size of these data sets has made efficient exploration of them impossible . To aid scientists in gleaning insight from their simulation data , we have developed an ad-hoc query infrastructure . Our system , called AQSim ( short for Ad-hoc Queries for Simulation ) reduces the data storage requirements and query access times in two stages . First , it creates and stores mathematical and statistical models of the data at multiple resolutions . Second , it evaluates queries on the models of the data instead of on the entire data set . In this paper , we present two simple but effective statistical modeling techniques for simulation data . Our first modeling technique computes the `` true '' ( unbiased ) mean of systematic partitions of the data . It makes no assumptions about the distribution of the data and uses a variant of the root mean square error to evaluate a model . Our second statistical modeling technique uses the Andersen-Darling goodness-of-fit method on systematic partitions of the data . This method evaluates a model by how well it passes the normality test on the data . Both of our statistical models effectively answer range queries . At each resolution of the data , we compute the precision of our answer to the user 's query by scaling the one-sided Chebyshev Inequalities with the original mesh 's topology . We combine precisions at different resolutions by calculating their weighted average . Our experimental evaluations on two scientific simulation data sets illustrate the value of using these statistical modeling techniques on multiple resolutions of large simulation data sets .

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

approximate ad-hoc queries
large-scale scientific data sets


Learning patterns in the dynamics of biological networks Our dynamic graph-based relational mining approach has been developed to learn structural patterns in biological networks as they change over time . The analysis of dynamic networks is important not only to understand life at the system-level , but also to discover novel patterns in other structural data . Most current graph-based data mining approaches overlook dynamic features of biological networks , because they are focused on only static graphs . Our approach analyzes a sequence of graphs and discovers rules that capture the changes that occur between pairs of graphs in the sequence . These rules represent the graph rewrite rules that the first graph must go through to be isomorphic to the second graph . Then , our approach feeds the graph rewrite rules into a machine learning system that learns general transformation rules describing the types of changes that occur for a class of dynamic biological networks . The discovered graph-rewriting rules show how biological networks change over time , and the transformation rules show the repeated patterns in the structural changes . In this paper , we apply our approach to biological networks to evaluate our approach and to understand how the biosystems change over time . We evaluate our results using coverage and prediction metrics , and compare to biological literature .

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 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

biological network
dynamic network analysis
graph mining
graph rewriting rule


Mining scale-free networks using geodesic clustering Many real-world graphs have been shown to be scale-free -- vertex degrees follow power law distributions , vertices tend to cluster , and the average length of all shortest paths is small . We present a new model for understanding scale-free networks based on multilevel geodesic approximation , using a new data structure called a multilevel mesh . Using this multilevel framework , we propose a new kind of graph clustering for data reduction of very large graph systems such as social , biological , or electronic networks . Finally , we apply our algorithms to real-world social networks and protein interaction graphs to show that they can reveal knowledge embedded in underlying graph structures . We also demonstrate how our data structures can be used to quickly answer approximate distance and shortest path queries on scale-free networks .

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


Pervasive parallelism in data mining : dataflow solution to co-clustering large and sparse Netflix data All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems . In this paper , we describe an efficient dataflow implementation of a collaborative filtering ( CF ) solution to the Netflix Prize problem ( 1 ) based on weighted coclustering ( 5 ) . The dataflow library we use facilitates the development of sophisticated parallel programs designed to fully utilize commodity multicore hardware , while hiding traditional difficulties such as queuing , threading , memory management , and deadlocks . The dataflow CF implementation first compresses the large , sparse training dataset into co-clusters . Then it generates recommendations by combining the average ratings of the co-clusters with the biases of the users and movies . When configured to identify 20x20 co-clusters in the Netflix training dataset , the implementation predicted over 100 million ratings in 16.31 minutes and achieved an RMSE of 0.88846 without any fine-tuning or domain knowledge . This is an effective real-time prediction runtime of 9.7 us per rating which is far superior to previously reported results . Moreover , the implemented co-clustering framework supports a wide variety of other large-scale data mining applications and forms the basis for predictive modeling on large , dyadic datasets ( 4 , 7 ) .

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


Clustering pair-wise dissimilarity data into partially ordered sets Ontologies represent data relationships as hierarchies of possibly overlapping classes . Ontologies are closely related to clustering hierarchies , and in this article we explore this relationship in depth . In particular , we examine the space of ontologies that can be generated by pairwise dissimilarity matrices . We demonstrate that classical clustering algorithms , which take dissimilarity matrices as inputs , do not incorporate all available information . In fact , only special types of dissimilarity matrices can be exactly preserved by previous clustering methods . We model ontologies as a partially ordered set ( poset ) over the subset relation . In this paper , we propose a new clustering algorithm , that generates a partially ordered set of clusters from a dissimilarity matrix .

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


Mining distance-based outliers in near linear time with randomization and a simple pruning rule Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set . Recently , much work has been conducted with the goal of finding fast algorithms for this task . We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used . We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude . Our average case analysis suggests that much of the efficiency is because the time to process non-outliers , which are the majority of examples , does not depend on the size of the data set .

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

anomaly detection
diskbased algorithms
distance-based operations


Dense itemsets Frequent itemset mining has been the subject of a lot of work in data mining research ever since association rules were introduced . In this paper we address a problem with frequent itemsets : that they only count rows where all their attributes are present , and do not allow for any noise . We show that generalizing the concept of frequency while preserving the performance of mining algorithms is nontrivial , and introduce a generalization of frequent itemsets , dense itemsets . Dense itemsets do not require all attributes to be present at the same time ; instead , the itemset needs to define a sufficiently large submatrix that exceeds a given density threshold of attributes present . We consider the problem of computing all dense itemsets in a database . We give a levelwise algorithm for this problem , and also study the top - $ k $ variations , i.e. , finding the k densest sets with a given support , or the k best-supported sets with a given density . These algorithms select the other parameter automatically , which simplifies mining dense itemsets in an explorative way . We show that the concept captures natural facets of data sets , and give extensive empirical results on the performance of the algorithms . Combining the concept of dense itemsets with set cover ideas , we also show that dense itemsets can be used to obtain succinct descriptions of large datasets . We also discuss some variations of dense itemsets .

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


Fast direction-aware proximity for graph mining In this paper we study asymmetric proximity measures on directed graphs , which quantify the relationships between two nodes or two groups of nodes . The measures are useful in several graph mining tasks , including clustering , link prediction and connection subgraph discovery . Our proximity measure is based on the conceptof escape probability . This way , we strive to summarize the multiple facets of nodes-proximity , while avoiding some of the pitfalls to which alternative proximity measures are susceptible . A unique feature of the measures is accounting for the underlying directional information . We put a special emphasis on computational efficiency , and develop fast solutions that are applicable in several settings . Our experimental study shows the usefulness of our proposed direction-aware proximity method for several applications , and that our algorithms achieve a significant speedup ( up to 50,000 x ) over straight forward implementations .

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


A framework for specifying explicit bias for revision of approximate information extraction rules

text mining
theory revision
user guided revision


Translation-invariant mixture models for curve clustering In this paper we present a family of algorithms that can simultaneously align and cluster sets of multidimensional curves defined on a discrete time grid . Our approach uses the Expectation-Maximization ( EM ) algorithm to recover both the mean curve shapes for each cluster , and the most likely shifts , offsets , and cluster memberships for each curve . We demonstrate how Bayesian estimation methods can improve the results for small sample sizes by enforcing smoothness in the cluster mean curves . We evaluate the methodology on two real-world data sets , time-course gene expression data and storm trajectory data . Experimental results show that models that incorporate curve alignment systematically provide improvements in predictive power and within-cluster variance on test data sets . The proposed approach provides a non-parametric , computationally efficient , and robust methodology for clustering broad classes of curve data .

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

learning
mixture model
transformation invariance


Mobile call graphs : beyond power-law and lognormal distributions We analyze a massive social network , gathered from the records of a large mobile phone operator , with more than a million users and tens of millions of calls . We examine the distributions of the number of phone calls per customer ; the total talk minutes per customer ; and the distinct number of calling partners per customer . We find that these distributions are skewed , and that they significantly deviate from what would be expected by power-law and lognormal distributions . To analyze our observed distributions ( of number of calls , distinct call partners , and total talk time ) , we propose PowerTrack , a method which fits a lesser known but more suitable distribution , namely the Double Pareto LogNormal ( DPLN ) distribution , to our data and track its parameters over time . Using PowerTrack , we find that our graph changes over time in a way consistent with a generative process that naturally results in the DPLN distributions we observe . Furthermore , we show that this generative process lends itself to a natural and appealing social wealth interpretation in the context of social networks such as ours . We discuss the application of those results to our model and to forecasting .

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


A cross-collection mixture model for comparative text mining In this paper , we define and study a novel text mining problem , which we refer to as Comparative Text Mining ( CTM ) . Given a set of comparable text collections , the task of comparative text mining is to discover any latent common themes across all collections as well as summarize the similarity and differences of these collections along each common theme . This general problem subsumes many interesting applications , including business intelligence and opinion summarization . We propose a generative probabilistic mixture model for comparative text mining . The model simultaneously performs cross-collection clustering and within-collection clustering , and can be applied to an arbitrary set of comparable text collections . The model can be estimated efficiently using the Expectation-Maximization ( EM ) algorithm . We evaluate the model on two different text data sets ( i.e. , a news article data set and a laptop review data set ) , and compare it with a baseline clustering method also based on a mixture model . Experiment results show that the model is quite effective in discovering the latent common themes across collections and performs significantly better than our baseline mixture model .

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 B I I O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O 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
mixture models


Anonymity-preserving data collection Protection of privacy has become an important problem in data mining . In particular , individuals have become increasingly unwilling to share their data , frequently resulting in individuals either refusing to share their data or providing incorrect data . In turn , such problems in data collection can affect the success of data mining , which relies on sufficient amounts of accurate data in order to produce meaningful results . Random perturbation and randomized response techniques can provide some level of privacy in data collection , but they have an associated cost in accuracy . Cryptographic privacy-preserving data mining methods provide good privacy and accuracy properties . However , in order to be efficient , those solutions must be tailored to specific mining tasks , thereby losing generality . In this paper , we propose efficient cryptographic techniques for online data collection in which data from a large number of respondents is collected anonymously , without the help of a trusted third party . That is , our solution allows the miner to collect the original data from each respondent , but in such a way that the miner can not link a respondent 's data to the respondent . An advantage of such a solution is that , because it does not change the actual data , its success does not depend on the underlying data mining problem . We provide proofs of the correctness and privacy of our solution , as well as experimental data that demonstrates its efficiency . We also extend our solution to tolerate certain kinds of malicious behavior of the participants .

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


Mining statistically important equivalence classes and delta-discriminative emerging patterns The support-confidence framework is the most common measure used in itemset mining algorithms , for its antimonotonicity that effectively simplifies the search lattice . This computational convenience brings both quality and statistical flaws to the results as observed by many previous studies . In this paper , we introduce a novel algorithm that produces itemsets with ranked statistical merits under sophisticated test statistics such as chi-square , risk ratio , odds ratio , etc. . Our algorithm is based on the concept of equivalence classes . An equivalence class is a set of frequent itemsets that always occur together in the same set of transactions . Therefore , itemsets within an equivalence class all share the same level of statistical significance regardless of the variety of test statistics . As an equivalence class can be uniquely determined and concisely represented by a closed pattern and a set of generators , we just mine closed patterns and generators , taking a simultaneous depth-first search scheme . This parallel approach has not been exploited by any prior work . We evaluate our algorithm on two aspects . In general , we compare to LCM and FPclose which are the best algorithms tailored for mining only closed patterns . In particular , we compare to epMiner which is the most recent algorithm for mining a type of relative risk patterns , known as minimal emerging patterns . Experimental results show that our algorithm is faster than all of them , sometimes even multiple orders of magnitude faster . These statistically ranked patterns and the efficiency have a high potential for real-life applications , especially in biomedical and financial fields where classical test statistics are of dominant interest .

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


Structured correspondence topic models for mining captioned figures in biological literature A major source of information ( often the most crucial and informative part ) in scholarly articles from scientific journals , proceedings and books are the figures that directly provide images and other graphical illustrations of key experimental results and other scientific contents . In biological articles , a typical figure often comprises multiple panels , accompanied by either scoped or global captioned text . Moreover , the text in the caption contains important semantic entities such as protein names , gene ontology , tissues labels , etc. , relevant to the images in the figure . Due to the avalanche of biological literature in recent years , and increasing popularity of various bio-imaging techniques , automatic retrieval and summarization of biological information from literature figures has emerged as a major unsolved challenge in computational knowledge extraction and management in the life science . We present a new structured probabilistic topic model built on a realistic figure generation scheme to model the structurally annotated biological figures , and we derive an efficient inference algorithm based on collapsed Gibbs sampling for information retrieval and visualization . The resulting program constitutes one of the key IR engines in our SLIF system that has recently entered the final round ( 4 out 70 competing systems ) of the Elsevier Grand Challenge on Knowledge Enhancement in the Life Science . Here we present various evaluations on a number of data mining tasks to illustrate our method .

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 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

gibbs sampling
information retrieval
topic models


Mining adaptively frequent closed unlabeled rooted trees in data streams Closed patterns are powerful representatives of frequent patterns , since they eliminate redundant information . We propose a new approach for mining closed unlabeled rooted trees adaptively from data streams that change over time . Our approach is based on an efficient representation of trees and a low complexity notion of relaxed closed trees , and leads to an on-line strategy and an adaptive sliding window technique for dealing with changes over time . More precisely , we first present a general methodology to identify closed patterns in a data stream , using Galois Lattice Theory . Using this methodology , we then develop three closed tree mining algorithms : an incremental one IncTreeNat , a sliding-window based one , WinTreeNat , and finally one that mines closed trees adaptively from data streams , AdaTreeNat . To the best of our knowledge this is the first work on mining frequent closed trees in streaming data varying with time . We give a first experimental evaluation of the proposed algorithms .

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


Empirical comparisons of various voting methods in bagging Finding effective methods for developing an ensemble of models has been an active research area of large-scale data mining in recent years . Models learned from data are often subject to some degree of uncertainty , for a variety of resoans . In classification , ensembles of models provide a useful means of averaging out error introduced by individual classifiers , hence reducing the generalization error of prediction . The plurality voting method is often chosen for bagging , because of its simplicity of implementation . However , the plurality approach to model reconciliation is ad-hoc . There are many other voting methods to choose from , including the anti-plurality method , the plurality method with elimination , the Borda count method , and Condorcet 's method of pairwise comparisons . Any of these could lead to a better method for reconciliation . In this paper , we analyze the use of these voting methods in model reconciliation . We present empirical results comparing performance of these voting methods when applied in bagging . These results include some surprises , and among other things suggest that ( 1 ) plurality is not always the best voting method ; ( 2 ) the number of classes can affect the performance of voting methods ; and ( 3 ) the degree of dataset noise can affect the performance of voting methods . While it is premature to make final judgments about specific voting methods , the results of this work raise interesting questions , and they open the door to the application of voting theory in classification theory .

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


An efficient algorithm for a class of fused lasso problems The fused Lasso penalty enforces sparsity in both the coefficients and their successive differences , which is desirable for applications with features ordered in some meaningful way . The resulting problem is , however , challenging to solve , as the fused Lasso penalty is both non-smooth and non-separable . Existing algorithms have high computational complexity and do not scale to large-size problems . In this paper , we propose an Efficient Fused Lasso Algorithm ( EFLA ) for optimizing this class of problems . One key building block in the proposed EFLA is the Fused Lasso Signal Approximator ( FLSA ) . To efficiently solve FLSA , we propose to reformulate it as the problem of finding an `` appropriate '' subgradient of the fused penalty at the minimizer , and develop a Subgradient Finding Algorithm ( SFA ) . We further design a restart technique to accelerate the convergence of SFA , by exploiting the special `` structures '' of both the original and the reformulated FLSA problems . Our empirical evaluations show that , both SFA and EFLA significantly outperform existing solvers . We also demonstrate several applications of the fused Lasso .

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


Cryptographically private support vector machines We propose private protocols implementing the Kernel Adatron and Kernel Perceptron learning algorithms , give private classification protocols and private polynomial kernel computation protocols . The new protocols return their outputs - either the kernel value , the classifier or the classifications - in encrypted form so that they can be decrypted only by a common agreement by the protocol participants . We show how to use the encrypted classifications to privately estimate many properties of the data and the classifier . The new SVM classifiers are the first to be proven private according to the standard cryptographic definitions .

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

kernel methods
privacy preserving data mining


Integration of profile hidden Markov model output into association rule mining Scientific models typically depend on parameters . Preserving the parameter dependence of models in the pattern mining context opens up several applications . Within association rule mining ( ARM ) , the choice of parameters can be studied with more flexibly then in traditional model building . Studying support , confidence , and other rule metrics as a function of model parameters allows conclusions on assumptions underlying the models . We present efficient techniques to handle multiple model output data sets at little more than the cost of one . We integrate output from hidden Markov models into the association rule mining framework , demonstrating the potential for frequent pattern mining in the field of scientific modeling and experimentation .

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

association rule mining
profile hidden markov model


Query-time entity resolution The goal of entity resolution is to reconcile database references corresponding to the same real-world entities . Given the abundance of publicly available databases where entities are not resolved , we motivate the problem of quickly processing queries that require resolved entities from such ` unclean ' databases . We propose a two-stage collective resolution strategy for processing queries . We then show how it can be performed on-the-fly by adaptively extracting and resolving those database references that are the most helpful for resolving the query . We validate our approach on two large real-world publication databases where we show the usefulness of collective resolution and at the same time demonstrate the need for adaptive strategies for query processing . We then show how the same queries can be answered in real time using our adaptive approach while preserving the gains of collective resolution .

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


Learning to predict train wheel failures This paper describes a successful but challenging application of data mining in the railway industry . The objective is to optimize maintenance and operation of trains through prognostics of wheel failures . In addition to reducing maintenance costs , the proposed technology will help improve railway safety and augment throughput . Building on established techniques from data mining and machine learning , we present a methodology to learn models to predict train wheel failures from readily available operational and maintenance data . This methodology addresses various data mining tasks such as automatic labeling , feature extraction , model building , model fusion , and evaluation . After a detailed description of the methodology , we report results from large-scale experiments . These results clearly show the great potential of this innovative application of data mining in the railway industry .

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

machine learning
methodology
model building
model fusion

decision support
model evaluation
wheel failure prediction


Estimating the global pagerank of web communities Localized search engines are small-scale systems that index a particular community on the web . They offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build , and can provide more precise and complete search capability over their relevant domains . One disadvantage such systems have over large-scale search engines is the lack of global PageRank values . Such information is needed to assess the value of pages in the localized search domain within the context of the web as a whole . In this paper , we present well-motivated algorithms to estimate the global PageRank values of a local domain . The algorithms are all highly scalable in that , given a local domain of size n , they use O ( n ) resources that include computation time , bandwidth , and storage . We test our methods across a variety of localized domains , including site-specific domains and topic-specific domains . We demonstrate that by crawling as few as n or 2n additional pages , our methods can give excellent global PageRank estimates .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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
markov chain
numerical linear algebra
page rank
stochastic complementation


Compressed data cubes for OLAP aggregate query approximation on continuous dimensions

approximate query answering
decision support
density estimation


Predicting prostate cancer recurrence via maximizing the concordance index In order to effectively use machine learning algorithms , e.g. , neural networks , for the analysis of survival data , the correct treatment of censored data is crucial . The concordance index ( CI ) is a typical metric for quantifying the predictive ability of a survival model . We propose a new algorithm that directly uses the CI as the objective function to train a model , which predicts whether an event will eventually occur or not . Directly optimizing the CI allows the model to make complete use of the information from both censored and non-censored observations . In particular , we approximate the CI via a differentiable function so that gradient-based methods can be used to train the model . We applied the new algorithm to predict the eventual recurrence of prostate cancer following radical prostatectomy . Compared with the traditional Cox proportional hazards model and several other algorithms based on neural networks and support vector machines , our algorithm achieves a significant improvement in being able to identify high-risk and low-risk groups of patients .

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

concordance index
neural networks
prostate cancer recurrence


Recovering latent time-series from their observed sums : network tomography with particle filters . Hidden variables , evolving over time , appear in multiple settings , where it is valuable to recover them , typically from observed sums . Our driving application is ` network tomography ' , where we need to estimate the origin-destination ( OD ) traffic flows to determine , e.g. , who is communicating with whom in a local area network . This information allows network engineers and managers to solve problems in design , routing , configuration debugging , monitoring and pricing . Unfortunately the direct measurement of the OD traffic is usually difficult , or even impossible ; instead , we can easily measure the loads on every link , that is , sums of desirable OD flows . In this paper we propose i-FILTER , a method to solve this problem , which improves the state-of-the-art by ( a ) introducing explicit time dependence , and by ( b ) using realistic , non-Gaussian marginals in the statistical models for the traffic flows , as never attempted before . We give experiments on real data , where i-FILTER scales linearly with new observations and out-performs the best existing solutions , in a wide variety of settings . Specifically , on real network traffic measured at CMU , and at AT&T , i-FILTER reduced the estimation errors between 15 % and 46 % in all cases .

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

self-organizing bayesian dynamical system


Generating non-redundant association rules


Generalizing the notion of support The goal of this paper is to show that generalizing the notion of support can be useful in extending association analysis to non-traditional types of patterns and non-binary data . To that end , we describe a framework for generalizing support that is based on the simple , but useful observation that support can be viewed as the composition of two functions : a function that evaluates the strength or presence of a pattern in each object ( transaction ) and a function that summarizes these evaluations with a single number . A key goal of any framework is to allow people to more easily express , explore , and communicate ideas , and hence , we illustrate how our support framework can be used to describe support for a variety of commonly used association patterns , such as frequent itemsets , general Boolean patterns , and error-tolerant itemsets . We also present two examples of the practical usefulness of generalized support . One example shows the usefulness of support functions for continuous data . Another example shows how the hyperclique pattern -- an association pattern originally defined for binary data -- can be extended to continuous data by generalizing a support function .

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

association analysis
hyperclique
support


Failure detection and localization in component based systems by online tracking The increasing complexity of today 's systems makes fast and accurate failure detection essential for their use in mission-critical applications . Various monitoring methods provide a large amount of data about system 's behavior . Analyzing this data with advanced statistical methods holds the promise of not only detecting the errors faster , but also detecting errors which are difficult to catch with current monitoring tools . Two challenges to building such detection tools are : the high dimensionality of observation data , which makes the models expensive to apply , and frequent system changes , which make the models expensive to update . In this paper , we present algorithms to reduce the dimensionality of data in a way that makes it easy to adapt to system changes . We decompose the observation data into signal and noise subspaces . Two statistics , the Hotelling T2 score and squared prediction error ( SPE ) are calculated to represent the data characteristics in signal and noise subspaces respectively . Instead of tracking the original data , we use a sequentially discounting expectation maximization ( SDEM ) algorithm to learn the distribution of the two extracted statistics . A failure event can then be detected based on the abnormal change of the distribution . Applying our technique to component interaction data in a simple e-commerce application shows better accuracy than building independent profiles for each component . Additionally , experiments on synthetic data show that the detection accuracy is high even for changing systems .

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

failure detection
online tracking
statistics

distributed computing
internet services
learning
subspace decomposition
system management


User grouping behavior in online forums Online forums represent one type of social media that is particularly rich for studying human behavior in information seeking and diffusing . The way users join communities is a reflection of the changing and expanding of their interests toward information . In this paper , we study the patterns of user participation behavior , and the feature factors that influence such behavior on different forum datasets . We find that , despite the relative randomness and lesser commitment of structural relationships in online forums , users ' community joining behaviors display some strong regularities . One particularly interesting observation is that the very weak relationships between users defined by online replies have similar diffusion curves as those of real friendships or co-authorships . We build social selection models , Bipartite Markov Random Field ( BiMRF ) , to quantitatively evaluate the prediction performance of those feature factors and their relationships . Using these models , we show that some features carry supplementary information , and the effectiveness of different features vary in different types of forums . Moreover , the results of BiMRF with two-star configurations suggest that the feature of user similarity defined by frequency of communication or number of common friends is inadequate to predict grouping behavior , but adding node-level features can improve the fit of the model .

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

information diffusion
social networks
social selection model


Formulating distance functions via the kernel trick Tasks of data mining and information retrieval depend on a good distance function for measuring similarity between data instances . The most effective distance function must be formulated in a context-dependent ( also application - , data - , and user-dependent ) way . In this paper , we propose to learn a distance function by capturing the nonlinear relationships among contextual information provided by the application , data , or user . We show that through a process called the `` kernel trick , '' such nonlinear relationships can be learned efficiently in a projected space . Theoretically , we substantiate that our method is both sound and optimal . Empirically , using several datasets and applications , we demonstrate that our method is effective and useful .

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


Efficient influence maximization in social networks Influence maximization is the problem of finding a small subset of nodes ( seed nodes ) in a social network that could maximize the spread of influence . In this paper , we study the efficient influence maximization from two complementary directions . One is to improve the original greedy algorithm of ( 5 ) and its improvement ( 7 ) to further reduce its running time , and the second is to propose new degree discount heuristics that improves influence spread . We evaluate our algorithms by experiments on two large academic collaboration graphs obtained from the online archival database arXiv.org . Our experimental results show that ( a ) our improved greedy algorithm achieves better running time comparing with the improvement of ( 7 ) with matching influence spread , ( b ) our degree discount heuristics achieve much better influence spread than classic degree and centrality-based heuristics , and when tuned for a specific influence cascade model , it achieves almost matching influence thread with the greedy algorithm , and more importantly ( c ) the degree discount heuristics run only in milliseconds while even the improved greedy algorithms run in hours in our experiment graphs with a few tens of thousands of nodes . Based on our results , we believe that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time . Therefore , contrary to what implied by the conclusion of ( 5 ) that traditional heuristics are outperformed by the greedy approximation algorithm , our results shed new lights on the research of heuristic algorithms .

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

heuristic algorithms
influence maximization
social networks


Regularized discriminant analysis for high dimensional , low sample size data Linear and Quadratic Discriminant Analysis have been used widely in many areas of data mining , machine learning , and bioinformatics . Friedman proposed a compromise between Linear and Quadratic Discriminant Analysis , called Regularized Discriminant Analysis ( RDA ) , which has been shown to be more flexible in dealing with various class distributions . RDA applies the regularization techniques by employing two regularization parameters , which are chosen to jointly maximize the classification performance . The optimal pair of parameters is commonly estimated via cross-validation from a set of candidate pairs . It is computationally prohibitive for high dimensional data , especially when the candidate set is large , which limits the applications of RDA to low dimensional data . In this paper , a novel algorithm for RDA is presented for high dimensional data . It can estimate the optimal regularization parameters from a large set of parameter candidates efficiently . Experiments on a variety of datasets confirm the claimed theoretical estimate of the efficiency , and also show that , for a properly chosen pair of regularization parameters , RDA performs favorably in classification , in comparison with other existing classification methods .

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

quadratic discriminant analysis
regularization


Compression of weighted graphs We propose to compress weighted graphs ( networks ) , motivated by the observation that large networks of social , biological , or other relations can be complex to handle and visualize . In the process also known as graph simplification , nodes and ( unweighted ) edges are grouped to supernodes and superedges , respectively , to obtain a smaller graph . We propose models and algorithms for weighted graphs . The interpretation ( i.e. decompression ) of a compressed , weighted graph is that a pair of original nodes is connected by an edge if their supernodes are connected by one , and that the weight of an edge is approximated to be the weight of the superedge . The compression problem now consists of choosing supernodes , superedges , and superedge weights so that the approximation error is minimized while the amount of compression is maximized . In this paper , we formulate this task as the ` simple weighted graph compression problem ' . We then propose a much wider class of tasks under the name of ` generalized weighted graph compression problem ' . The generalized task extends the optimization to preserve longer-range connectivities between nodes , not just individual edge weights . We study the properties of these problems and propose a range of algorithms to solve them , with different balances between complexity and quality of the result . We evaluate the problems and algorithms experimentally on real networks . The results indicate that weighted graphs can be compressed efficiently with relatively little compression error .

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


Mining a stream of transactions for customer patterns Transaction data can arrive at a ferocious rate in the order that transactions are completed . The data contain an enormous amount of information about customers , not just transactions , but extracting up-to-date customer information from an ever changing stream of data and mining it in real-time is a challenge . This paper describes a statistically principled approach to designing short , accurate summaries or signatures of high dimensional customer behavior that can be kept current with a stream of transactions . A signature database can then be used for data mining and to provide approximate answers to many kinds of queries about current customers quickly and accurately , as an empirical study of the calling patterns of 96,000 wireless customers who made about 18 million wireless calls over a three month period shows .

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

approximate queries
customer profiles
dynamic database
histograms
incremental updates
massive data


Learning to match and cluster large high-dimensional data sets for data integration Part of the process of data integration is determining which sets of identifiers refer to the same real-world entities . In integrating databases found on the Web or obtained by using information extraction methods , it is often possible to solve this problem by exploiting similarities in the textual names used for objects in different databases . In this paper we describe techniques for clustering and matching identifier names that are both scalable and adaptive , in the sense that they can be trained to obtain better performance in a particular domain . An experimental evaluation on a number of sample datasets shows that the adaptive method sometimes performs much better than either of two non-adaptive baseline systems , and is nearly always competitive with the best baseline system .

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


On mining cross-graph quasi-cliques Joint mining of multiple data sets can often discover interesting , novel , and reliable patterns which can not be obtained solely from any single source . For example , in cross-market customer segmentation , a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market . As another example , in bioinformatics , by joint mining of gene expression data and protein interaction data , we can find clusters of genes which show coherent expression patterns and also produce interacting proteins . Such clusters may be potential pathways . In this paper , we investigate a novel data mining problem , mining cross-graph quasi-cliques , which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data . We build a general model for mining cross-graph quasi-cliques , show why the complete set of cross-graph quasi-cliques can not be found by previous data mining methods , and study the complexity of the problem . While the problem is difficult , we develop an efficient algorithm , Crochet , which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques . A systematic performance study is reported on both synthetic and real data sets . We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics . The experimental results also show that algorithm Crochet is efficient and scalable .

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


Training structural svms with kernels using sampled cuts Discriminative training for structured outputs has found increasing applications in areas such as natural language processing , bioinformatics , information retrieval , and computer vision . Focusing on large-margin methods , the most general ( in terms of loss function and model structure ) training algorithms known to date are based on cutting-plane approaches . While these algorithms are very efficient for linear models , their training complexity becomes quadratic in the number of examples when kernels are used . To overcome this bottleneck , we propose new training algorithms that use approximate cutting planes and random sampling to enable efficient training with kernels . We prove that these algorithms have improved time complexity while providing approximation guarantees . In empirical evaluations , our algorithms produced solutions with training and test error rates close to those of exact solvers . Even on binary classification problems where highly optimized conventional training methods exist ( e.g. SVM-light ) , our methods are about an order of magnitude faster than conventional training methods on large datasets , while remaining competitive in speed on datasets of medium size .

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

large-scale problems
learning
support vector machines


Generalized clustering , supervised learning , and data assignment Clustering algorithms have become increasingly important in handling and analyzing data . Considerable work has been done in devising effective but increasingly specific clustering algorithms . In contrast , we have developed a generalized framework that accommodates diverse clustering algorithms in a systematic way . This framework views clustering as a general process of iterative optimization that includes modules for supervised learning and instance assignment . The framework has also suggested several novel clustering methods . In this paper , we investigate experimentally the efficacy of these algorithms and test some hypotheses about the relation between such unsupervised techniques and the supervised methods embedded in them .

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

clustering
iterative optimization
learning