Meme-tracking and the dynamics of the news cycle Tracking new topics , ideas , and `` memes '' across the Web has been an issue of considerable interest . Recent work has developed methods for tracking topic shifts over long time scales , as well as abrupt spikes in the appearance of particular named entities . However , these approaches are less well suited to the identification of content that spreads widely and then fades over time scales on the order of days - the time scale at which we perceive news and events . We develop a framework for tracking short , distinctive phrases that travel relatively intact through on-line text ; developing scalable algorithms for clustering textual variants of such phrases , we identify a broad class of memes that exhibit wide spread and rich variation on a daily basis . As our principal domain of study , we show how such a meme-tracking approach can provide a coherent representation of the news cycle - the daily rhythms in the news media that have long been the subject of qualitative interpretation but have never been captured accurately enough to permit actual quantitative analysis . We tracked 1.6 million mainstream media sites and blogs over a period of three months with the total of 90 million articles and we find a set of novel and persistent temporal patterns in the news cycle . In particular , we observe a typical lag of 2.5 hours between the peaks of attention to a phrase in the news media and in blogs respectively , with divergent behavior around the overall peak and a `` heartbeat '' - like pattern in the handoff between news and blogs . We also develop and analyze a mathematical model for the kinds of temporal variation that the system exhibits .

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

information cascades
information networks
meme-tracking
social networks


Joint optimization of wrapper generation and template detection Many websites have large collections of pages generated dynamically from an underlying structured source like a database . The data of a category are typically encoded into similar pages by a common script or template . In recent years , some value-added services , such as comparison shopping and vertical search in a specific domain , have motivated the research of extraction technologies with high accuracy . Almost all previous works assume that input pages of a wrapper induction system conform to a common template and they can be easily identified in terms of a common schema of URL . However , we observed that it is hard to distinguish different templates using dynamic URLs today . Moreover , since extraction accuracy heavily depends on how consistent input pages are , we argue that it is risky to determine whether pages share a common template solely based on URLs . Instead , we propose a new approach that utilizes similarity between pages to detect templates . Our approach separates pages with notable inner differences and then generates wrappers , respectively . Experimental results show that our proposed approach is feasible and effective for improving extraction accuracy .

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


Ranking-based classification of heterogeneous information networks It has been recently recognized that heterogeneous information networks composed of multiple types of nodes and links are prevalent in the real world . Both classification and ranking of the nodes ( or data objects ) in such networks are essential for network analysis . However , so far these approaches have generally been performed separately . In this paper , we combine ranking and classification in order to perform more accurate analysis of a heterogeneous information network . Our intuition is that highly ranked objects within a class should play more important roles in classification . On the other hand , class membership information is important for determining a quality ranking over a dataset . We believe it is therefore beneficial to integrate classification and ranking in a simultaneous , mutually enhancing process , and to this end , propose a novel ranking-based iterative classification framework , called RankClass . Specifically , we build a graph-based ranking model to iteratively compute the ranking distribution of the objects within each class . At each iteration , according to the current ranking results , the graph structure used in the ranking algorithm is adjusted so that the sub-network corresponding to the specific class is emphasized , while the rest of the network is weakened . As our experiments show , integrating ranking with classification not only generates more accurate classes than the state-of-art classification methods on networked data , but also provides meaningful ranking of objects within each class , serving as a more informative view of the data than traditional classification .

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

classification
heterogeneous information network
ranking


On compressing social networks Motivated by structural properties of the Web graph that support efficient data structures for in memory adjacency queries , we study the extent to which a large network can be compressed . Boldi and Vigna ( WWW 2004 ) , showed that Web graphs can be compressed down to three bits of storage per edge ; we study the compressibility of social networks where again adjacency queries are a fundamental primitive . To this end , we propose simple combinatorial formulations that encapsulate efficient compressibility of graphs . We show that some of the problems are NP-hard yet admit effective heuristics , some of which can exploit properties of social networks such as link reciprocity . Our extensive experiments show that social networks and the Web graph exhibit vastly different compressibility characteristics .

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


Suppressing model overfitting in mining concept-drifting data streams Mining data streams of changing class distributions is important for real-time business decision support . The stream classifier must evolve to reflect the current class distribution . This poses a serious challenge . On the one hand , relying on historical data may increase the chances of learning obsolete models . On the other hand , learning only from the latest data may lead to biased classifiers , as the latest data is often an unrepresentative sample of the current class distribution . The problem is particularly acute in classifying rare events , when , for example , instances of the rare class do not even show up in the most recent training data . In this paper , we use a stochastic model to describe the concept shifting patterns and formulate this problem as an optimization one : from the historical and the current training data that we have observed , find the most-likely current distribution , and learn a classifier based on the most-likely distribution . We derive an analytic solution and approximate this solution with an efficient algorithm , which calibrates the influence of historical data carefully to create an accurate classifier . We evaluate our algorithm with both synthetic and real-world datasets . Our results show that our algorithm produces accurate and efficient classification .

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 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 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


Topical query decomposition We introduce the problem of query decomposition , where we are given a query and a document retrieval system , and we want to produce a small set of queries whose union of resulting documents corresponds approximately to that of the original query . Ideally , these queries should represent coherent , conceptually well-separated topics . We provide an abstract formulation of the query decomposition problem , and we tackle it from two different perspectives . We first show how the problem can be instantiated as a specific variant of a set cover problem , for which we provide an efficient greedy algorithm . Next , we show how the same problem can be seen as a constrained clustering problem , with a very particular kind of constraint , i.e. , clustering with predefined clusters . We develop a two-phase algorithm based on hierarchical agglomerative clustering followed by dynamic programming . Our experiments , conducted on a set of actual queries in a Web scale search engine , confirm the effectiveness of the proposed solutions .

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

communications applications
query recommendation


A multiple tree algorithm for the efficient association of asteroid observations In this paper we examine the problem of efficiently finding sets of observations that conform to a given underlying motion model . While this problem is often phrased as a tracking problem , where it is called track initiation , it is useful in a variety of tasks where we want to find correspondences or patterns in spatial-temporal data . Unfortunately , this problem often suffers from a combinatorial explosion in the number of potential sets that must be evaluated . We consider the problem with respect to large-scale asteroid observation data , where the goal is to find associations among the observations that correspond to the same underlying asteroid . In this domain , it is vital that we can efficiently extract the underlying associations . We introduce a new methodology for track initiation that exhaustively considers all possible linkages . We then introduce an exact tree-based algorithm for tractably finding all compatible sets of points . Further , we extend this approach to use multiple trees , exploiting structure from several time steps at once . We compare this approach to a standard sequential approach and show how the use of multiple trees can provide a significant benefit .

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


Inferring networks of diffusion and influence Information diffusion and virus propagation are fundamental processes talking place in networks . While it is often possible to directly observe when nodes become infected , observing individual transmissions ( i.e. , who infects whom or who influences whom ) is typically very difficult . Furthermore , in many applications , the underlying network over which the diffusions and propagations spread is actually unobserved . We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate . Given the times when nodes adopt pieces of information or become infected , we identify the optimal network that best explains the observed infection times . Since the optimization problem is NP-hard to solve exactly , we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance . We demonstrate the effectiveness of our approach by tracing information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space . We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web . These sites tend to have stable circles of influence with more general news media sites acting as connectors between them .

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

blogs
information cascades
networks of diffusion
news media


Sustainable operation and management of data center chillers using temporal data mining Motivation : Data centers are a critical component of modern IT infrastructure but are also among the worst environmental offenders through their increasing energy usage and the resulting large carbon footprints . Efficient management of data centers , including power management , networking , and cooling infrastructure , is hence crucial to sustainability . In the absence of a ` first-principles ' approach to manage these complex components and their interactions , data-driven approaches have become attractive and tenable . Results : We present a temporal data mining solution to model and optimize performance of data center chillers , a key component of the cooling infrastructure . It helps bridge raw , numeric , time-series information from sensor streams toward higher level characterizations of chiller behavior , suitable for a data center engineer . To aid in this transduction , temporal data streams are first encoded into a symbolic representation , next run-length encoded segments are mined to form frequent motifs in time series , and finally these metrics are evaluated by their contributions to sustainability . A key innovation in our application is the ability to intersperse `` do n't care '' transitions ( e.g. , transients ) in continuous-valued time series data , an advantage we inherit by the application of frequent episode mining to symbolized representations of numeric time series . Our approach provides both qualitative and quantitative characterizations of the sensor streams to the data center engineer , to aid him in tuning chiller operating characteristics . This system is currently being prototyped for a data center managed by HP and experimental results from this application reveal the promise of our approach .

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

chillers
data centers
motifs
sustainability


Mining distance-based outliers from large databases in any metric space Let R be a set of objects . An object o ∈ R is an outlier , if there exist less than k objects in R whose distances to o are at most r. The values of k , r , and the distance metric are provided by a user at the run time . The objective is to return all outliers with the smallest I\/O cost . This paper considers a generic version of the problem , where no information is available for outlier computation , except for objects ' mutual distances . We prove an upper bound for the memory consumption which permits the discovery of all outliers by scanning the dataset 3 times . The upper bound turns out to be extremely low in practice , e.g. , less than 1 % of R. Since the actual memory capacity of a realistic DBMS is typically larger , we develop a novel algorithm , which integrates our theoretical findings with carefully-designed heuristics that leverage the additional memory to improve I\/O efficiency . Our technique reports all outliers by scanning the dataset at most twice ( in some cases , even once ) , and significantly outperforms the existing solutions by a factor up to an order of magnitude .

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

information search and retrieval
metric data


Detecting adversarial advertisements in the wild In a large online advertising system , adversaries may attempt to profit from the creation of low quality or harmful advertisements . In this paper , we present a large scale data mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users . Because both false positives and false negatives have high cost , our deployed system uses a tiered strategy combining automated and semi-automated methods to ensure reliable classification . We also employ strategies to address the challenges of learning from highly skewed data at scale , allocating the effort of human experts , leveraging domain expert knowledge , and independently assessing the effectiveness of our system .

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

adversarial learning
applications
online advertisement


Cluster-based concept invention for statistical relational learning We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning . Entities derived from clusters increase the expressivity of feature spaces by creating new first-class concepts which contribute to the creation of new features . For example , in CiteSeer , papers can be clustered based on words or citations giving `` topics '' , and authors can be clustered based on documents they co-author giving `` communities '' . Such cluster-derived concepts become part of more complex feature expressions . Out of the large number of generated features , those which improve predictive accuracy are kept in the model , as decided by statistical feature selection criteria . We present results demonstrating improved accuracy on two tasks , venue prediction and link prediction , using CiteSeer data .

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 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


DivRank : the interplay of prestige and diversity in information networks Information networks are widely used to characterize the relationships between data items such as text documents . Many important retrieval and mining tasks rely on ranking the data items based on their centrality or prestige in the network . Beyond prestige , diversity has been recognized as a crucial objective in ranking , aiming at providing a non-redundant and high coverage piece of information in the top ranked results . Nevertheless , existing network-based ranking approaches either disregard the concern of diversity , or handle it with non-optimized heuristics , usually based on greedy vertex selection . We propose a novel ranking algorithm , DivRank , based on a reinforced random walk in an information network . This model automatically balances the prestige and the diversity of the top ranked vertices in a principled way . DivRank not only has a clear optimization explanation , but also well connects to classical models in mathematics and network science . We evaluate DivRank using empirical experiments on three different networks as well as a text summarization task . DivRank outperforms existing network-based ranking methods in terms of enhancing diversity in prestige .

O O O O O O O B 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 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 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 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 B I 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 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

diversity
information networks
ranking
reinforced random walk


Scalable influence maximization for prevalent viral marketing in large-scale social networks Influence maximization , defined by Kempe , Kleinberg , and Tardos ( 2003 ) , is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models . The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks . Prior solutions , such as the greedy algorithm of Kempe et al. ( 2003 ) and its improvements are slow and not scalable , while other heuristic algorithms do not provide consistently good performance on influence spreads . In this paper , we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments . Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm . Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem : ( a ) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible , and ( b ) in all size ranges , our algorithm performs consistently well in influence spread -- it is always among the best algorithms , and in most cases it significantly outperforms all other scalable heuristics to as much as 100 % -- 260 % increase in influence spread .

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

influence maximization
social networks
viral marketing


The mathematics of causal inference I will review concepts , principles , and mathematical tools that were found useful in applications involving causal and counterfactual relationships . This semantical framework , enriched with a few ideas from logic and graph theory , gives rise to a complete , coherent , and friendly calculus of causation that unifies the graphical and counterfactual approaches to causation and resolves many long-standing problems in several of the sciences . These include questions of causal effect estimation , policy analysis , and the integration of data from diverse studies . Of special interest to KDD researchers would be the following topics : The Mediation Formula , and what it tells us about direct and indirect effects . What mathematics can tell us about `` external validity '' or `` generalizing from experiments '' What can graph theory tell us about recovering from sample-selection bias .

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


Algorithms for discovering bucket orders from data Ordering and ranking items of different types are important tasks in various applications , such as query processing and scientific data mining . A total order for the items can be misleading , since there are groups of items that have practically equal ranks . We consider bucket orders , i.e. , total orders with ties . They can be used to capture the essential order information without overfitting the data : they form a useful concept class between total orders and arbitrary partial orders . We address the question of finding a bucket order for a set of items , given pairwise precedence information between the items . We also discuss methods for computing the pairwise precedence data . We describe simple and efficient algorithms for finding good bucket orders . Several of the algorithms have a provable approximation guarantee , and they scale well to large datasets . We provide experimental results on artificial and a real data that show the usefulness of bucket orders and demonstrate the accuracy and efficiency of the algorithms .

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


Automatic multimedia cross-modal correlation discovery Given an image ( or video clip , or audio song ) , how do we automatically assign keywords to it ? The general problem is to find correlations across the media in a collection of multimedia objects like video clips , with colors , and\/or motion , and\/or audio , and\/or text scripts . We propose a novel , graph-based approach , `` MMG '' , to discover such cross-modal correlations . Our `` MMG '' method requires no tuning , no clustering , no user-determined constants ; it can be applied to any multimedia collection , as long as we have a similarity function for each medium ; and it scales linearly with the database size . We report auto-captioning experiments on the `` standard '' Corel image database of 680 MB , where it outperforms domain specific , fine-tuned methods by up to 10 percentage points in captioning accuracy ( 50 % relative improvement ) .

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

automatic image captioning
cross-modal correlation
graph-based model


Parallel mining of closed sequential patterns Discovery of sequential patterns is an essential data mining task with broad applications . Among several variations of sequential patterns , closed sequential pattern is the most useful one since it retains all the information of the complete pattern set but is often much more compact than it . Unfortunately , there is no parallel closed sequential pattern mining method proposed yet . In this paper we develop an algorithm , called Par-CSP ( Parallel Closed Sequential Pattern mining ) , to conduct parallel mining of closed sequential patterns on a distributed memory system . Par-CSP partitions the work among the processors by exploiting the divide-and-conquer property so that the overhead of interprocessor communication is minimized . Par-CSP applies dynamic scheduling to avoid processor idling . Moreover , it employs a technique , called selective sampling to address the load imbalance problem . We implement Par-CSP using MPI on a 64-node Linux cluster . Our experimental results show that Par-CSP attains good parallelization efficiencies on various input datasets .

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


Nestedness and segmented nestedness Consider each row of a 0-1 dataset as the subset of the columns for which the row has an 1 . Then a dataset is nested , if for all pairs of rows one row is either a superset or subset of the other . The concept of nestedness has its origins in ecology , where approximate versions of it has been used to model the species distribution in different locations . We argue that nestedness and its extensions are interesting properties of datasets , and that they can be applied also to domains other than ecology . We first define natural measures of nestedness and study their properties . We then define the concept of k-nestedness : a dataset is ( almost ) k-nested if the set of columns can be partitioned to k parts so that each part is ( almost ) nested . We consider the algorithmic problems of computing how far a dataset is from being k-nested , and for finding a good partition of the columns into k parts . The algorithms are based on spectral partitioning , and scale to moderately large datasets . We apply the methods to real data from ecology and from other applications , and demonstrate the usefulness of the concept .

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 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 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

0-1 matrices
nonnumerical algorithms and problems
presence/absence data


Enabling analysts in managed services for CRM analytics Data analytics tools and frameworks abound , yet rapid deployment of analytics solutions that deliver actionable insights from business data remains a challenge . The primary reason is that on-field practitioners are required to be both technically proficient and knowledgeable about the business . The recent abundance of unstructured business data has thrown up new opportunities for analytics , but has also multiplied the deployment challenge , since interpretation of concepts derived from textual sources require a deep understanding of the business . In such a scenario , a managed service for analytics comes up as the best alternative . A managed analytics service is centered around a business analyst who acts as a liaison between the business and the technology . This calls for new tools that assist the analyst to be efficient in the tasks that she needs to execute . Also , the analytics needs to be repeatable , in that the delivered insights should not depend heavily on the expertise of specific analysts . These factors lead us to identify new areas that open up for KDD research in terms of ` time-to-insight ' and repeatability for these analysts . We present our analytics framework in the form of a managed service offering for CRM analytics . We describe different analyst-centric tools using a case study from real-life engagements and demonstrate their effectiveness .

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


Active exploration for learning rankings from clickthrough data We address the task of learning rankings of documents from search enginelogs of user behavior . Previous work on this problem has relied onpassively collected clickthrough data . In contrast , we show that anactive exploration strategy can provide data that leads to much fasterlearning . Specifically , we develop a Bayesian approach for selectingrankings to present users so that interactions result in more informativetraining data . Our results using the TREC-10 Web corpus , as well assynthetic data , demonstrate that a directed exploration strategy quicklyleads to users being presented improved rankings in an online learningsetting . We find that active exploration substantially outperformspassive observation and random exploration .

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

information search and retrieval
learning to rank
web search


Fast query execution for retrieval models based on path-constrained random walks Many recommendation and retrieval tasks can be represented as proximity queries on a labeled directed graph , with typed nodes representing documents , terms , and metadata , and labeled edges representing the relationships between them . Recent work has shown that the accuracy of the widely-used random-walk-based proximity measures can be improved by supervised learning - in particular , one especially effective learning technique is based on Path-Constrained Random Walks ( PCRW ) , in which similarity is defined by a learned combination of constrained random walkers , each constrained to follow only a particular sequence of edge labels away from the query nodes . The PCRW based method significantly outperformed unsupervised random walk based queries , and models with learned edge weights . Unfortunately , PCRW query systems are expensive to evaluate . In this study we evaluate the use of approximations to the computation of the PCRW distributions , including fingerprinting , particle filtering , and truncation strategies . In experiments on several recommendation and retrieval problems using two large scientific publications corpora we show speedups of factors of 2 to 100 with little loss in accuracy .

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

filtering and recommending
information search and retrieval
learning to rank
path-constrained random walks
relational retrieval


Adaptive query processing for time-series data


Finding low-entropy sets and trees from binary data The discovery of subsets with special properties from binary data hasbeen one of the key themes in pattern discovery . Pattern classes suchas frequent itemsets stress the co-occurrence of the value 1 in the data . While this choice makes sense in the context of sparse binary data , it disregards potentially interesting subsets of attributes that have some other type of dependency structure . We consider the problem of finding all subsets of attributes that have low complexity . The complexity is measured by either the entropy of the projection of the data on the subset , or the entropy of the data for the subset when modeled using a Bayesian tree , with downward or upward pointing edges . We show that the entropy measure on sets has a monotonicity property , and thus a levelwise approach can find all low-entropy itemsets . We also show that the tree-based measures are bounded above by the entropy of the corresponding itemset , allowing similar algorithms to be used for finding low-entropy trees . We describe algorithms for finding all subsets satisfying an entropy condition . We give an extensive empirical evaluation of the performance of the methods both on synthetic and on real data . We also discuss the search for high-entropy subsets and the computation of the Vapnik-Chervonenkis dimension of the data .

O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 large-scale analysis of query logs for assessing personalization opportunities Query logs , the patterns of activity left by millions of users , contain a wealth of information that can be mined to aid personalization . We perform a large-scale study of Yahoo ! search engine logs , tracking 1.35 million browser-cookies over a period of 6 months . We define metrics to address questions such as 1 ) How much history is available ? , 2 ) How do users ' topical interests vary , as reflected by their queries ? , and 3 ) What can we learn from user clicks ? We find that there is significantly more expected history for the user of a randomly picked query than for a randomly picked user . We show that users exhibit consistent topical interests that vary between users . We also see that user clicks indicate a variety of special interests . Our findings shed light on user activity and can inform future personalization efforts .

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

categorization
clustering
miscellaneous
user history
user interests


Drosophila gene expression pattern annotation using sparse features and term-term interactions The Drosophila gene expression pattern images document the spatial and temporal dynamics of gene expression and they are valuable tools for explicating the gene functions , interaction , and networks during Drosophila embryogenesis . To provide text-based pattern searching , the images in the Berkeley Drosophila Genome Project ( BDGP ) study are annotated with ontology terms manually by human curators . We present a systematic approach for automating this task , because the number of images needing text descriptions is now rapidly increasing . We consider both improved feature representation and novel learning formulation to boost the annotation performance . For feature representation , we adapt the bag-of-words scheme commonly used in visual recognition problems so that the image group information in the BDGP study is retained . Moreover , images from multiple views can be integrated naturally in this representation . To reduce the quantization error caused by the bag-of-words representation , we propose an improved feature representation scheme based on the sparse learning technique . In the design of learning formulation , we propose a local regularization framework that can incorporate the correlations among terms explicitly . We further show that the resulting optimization problem admits an analytical solution . Experimental results show that the representation based on sparse learning outperforms the bag-of-words representation significantly . Results also show that incorporation of the term-term correlations improves the annotation performance consistently .

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

gene expression pattern
regularization
sparse learning


GBASE : a scalable and general graph management system Graphs appear in numerous applications including cyber-security , the Internet , social networks , protein networks , recommendation systems , and many more . Graphs with millions or even billions of nodes and edges are common-place . How to store such large graphs efficiently ? What are the core operations\/queries on those graph ? How to answer the graph queries quickly ? We propose GBASE , a scalable and general graph management and mining system . The key novelties lie in 1 ) our storage and compression scheme for a parallel setting and 2 ) the carefully chosen graph operations and their efficient implementation . We designed and implemented an instance of GBASE using MapReduce\/Hadoop . GBASE provides a parallel indexing mechanism for graph mining operations that both saves storage space , as well as accelerates queries . We ran numerous experiments on real graphs , spanning billions of nodes and edges , and we show that our proposed GBASE is indeed fast , scalable and nimble , with significant savings in space and time .

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 B 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 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 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


Cleaning disguised missing data : a heuristic approach In some applications such as filling in a customer information form on the web , some missing values may not be explicitly represented as such , but instead appear as potentially valid data values . Such missing values are known as disguised missing data , which may impair the quality of data analysis severely , such as causing significant biases and misleading results in hypothesis tests , correlation analysis and regressions . The very limited previous studies on cleaning disguised missing data use outlier mining and distribution anomaly detection . They highly rely on domain background knowledge in specific applications and may not work well for the cases where the disguise values are inliers . To tackle the problem of cleaning disguised missing data , in this paper , we first model the distribution of disguised missing data , and propose the embedded unbiased sample heuristic . Then , we develop an effective and efficient method to identify the frequently used disguise values which capture the major body of the disguised missing data . Our method does not require any domain background knowledge to find the suspicious disguise values . We report an empirical evaluation using real data sets , which shows that our method is effective - the frequently used disguise values found by our method match the values identified by the domain experts nicely . Our method is also efficient and scalable for processing large data sets .

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 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 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 I 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 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


Passenger-based predictive modeling of airline no-show rates Airlines routinely overbook flights based on the expectation that some fraction of booked passengers will not show for each flight . Accurate forecasts of the expected number of no-shows for each flight can increase airline revenue by reducing the number of spoiled seats ( empty seats that might otherwise have been sold ) and the number of involuntary denied boardings at the departure gate . Conventional no-show forecasting methods typically average the no-show rates of historically similar flights , without the use of passenger-specific information . We develop two classes of models to predict cabin-level no-show rates using specific information on the individual passengers booked on each flight . The first of these models computes the no-show probability for each passenger , using both the cabin-level historical forecast and the extracted passenger features as explanatory variables . This passenger-level model is implemented using three different predictive methods : a C4 .5 decision-tree , a segmented Naive Bayes algorithm , and a new aggregation method for an ensemble of probabilistic models . The second cabin-level model is formulated using the desired cabin-level no-show rate as the response variable . Inputs to this model include the predicted cabin-level no-show rates derived from the various passenger-level models , as well as simple statistics of the features of the cabin passenger population . The cabin-level model is implemented using either linear regression , or as a direct probability model with explicit incorporation of the cabin-level no-show rates derived from the passenger-level model outputs . The new passenger-based models are compared to a conventional historical model , using train and evaluation data sets taken from over 1 million passenger name records . Standard metrics such as lift curves and mean-square cabin-level errors establish the improved accuracy of the passenger-based models over the historical model . All models are also evaluated using a simple revenue model , and it is shown that the cabin-level passenger-based model can produce between 0.4 % and 3.2 % revenue gain over the conventional model , depending on the revenue-model parameters .

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

airline overbooking
classification
model aggregation
no-show forecasting
probabilistic estimation


A maximum entropy web recommendation system : combining collaborative and content features Web users display their preferences implicitly by navigating through a sequence of pages or by providing numeric ratings to some items . Web usage mining techniques are used to extract useful knowledge about user interests from such data . The discovered user models are then used for a variety of applications such as personalized recommendations . Web site content or semantic features of objects provide another source of knowledge for deciphering users ' needs or interests . We propose a novel Web recommendation system in which collaborative features such as navigation or rating data as well as the content features accessed by the users are seamlessly integrated under the maximum entropy principle . Both the discovered user patterns and the semantic relationships among Web objects are represented as sets of constraints that are integrated to fit the model . In the case of content features , we use a new approach based on Latent Dirichlet Allocation ( LDA ) to discover the hidden semantic relationships among items and derive constraints used in the model . Experiments on real Web site usage data sets show that this approach can achieve better recommendation accuracy , when compared to systems using only usage information . The integration of semantic information also allows for better interpretation of the generated recommendations .

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

maximum entropy
recommendation
web usage mining


Using relational knowledge discovery to prevent securities fraud We describe an application of relational knowledge discovery to a key regulatory mission of the National Association of Securities Dealers ( NASD ) . NASD is the world 's largest private-sector securities regulator , with responsibility for preventing and discovering misconduct among securities brokers . Our goal was to help focus NASD 's limited regulatory resources on the brokers who are most likely to engage in securities violations . Using statistical relational learning algorithms , we developed models that rank brokers with respect to the probability that they would commit a serious violation of securities regulations in the near future . Our models incorporate organizational relationships among brokers ( e.g. , past coworker ) , which domain experts consider important but have not been easily used before now . The learned models were subjected to an extensive evaluation using more than 18 months of data unseen by the model developers and comprising over two person weeks of effort by NASD staff . Model predictions were found to correlate highly with the subjective evaluations of experienced NASD examiners . Furthermore , in all performance measures , our models performed as well as or better than the handcrafted rules that are currently in use at NASD .

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

fraud detection
relational probability trees
statistical relational learning


Understandable models Of music collections based on exhaustive feature generation with temporal statistics Data mining in large collections of polyphonic music has recently received increasing interest by companies along with the advent of commercial online distribution of music . Important applications include the categorization of songs into genres and the recommendation of songs according to musical similarity and the customer 's musical preferences . Modeling genre or timbre of polyphonic music is at the core of these tasks and has been recognized as a difficult problem . Many audio features have been proposed , but they do not provide easily understandable descriptions of music . They do not explain why a genre was chosen or in which way one song is similar to another . We present an approach that combines large scale feature generation with meta learning techniques to obtain meaningful features for musical similarity . We perform exhaustive feature generation based on temporal statistics and train regression models to summarize a subset of these features into a single descriptor of a particular notion of music . Using several such models we produce a concise semantic description of each song . Genre classification models based on these semantic features are shown to be better understandable and almost as accurate as traditional methods .

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

feature generation
genre classification
meta learning

logistic regression
miscellaneous
music mining


Towards bounding sequential patterns Given a sequence database , can we have a non-trivial upper bound on the number of sequential patterns ? The problem of bounding sequential patterns is very challenging in theory due to the combinatorial complexity of sequences , even given some inspiring results on bounding itemsets in frequent itemset mining . Moreover , the problem is highly meaningful in practice , since the upper bound can be used in many applications such as space allocation in building sequence data warehouses . In this paper , we tackle the problem of bounding sequential patterns by presenting , for the first time in the field of sequential pattern mining , strong combinatorial results on computing the number of possible sequential patterns that can be generated at a given length k. We introduce , as a case study , two novel techniques to estimate the number of candidate sequences . An extensive empirical study on both real data and synthetic data verifies the effectiveness of our methods .

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


Fast nearest-neighbor search in disk-resident graphs Link prediction , personalized graph search , fraud detection , and many such graph mining problems revolve around the computation of the most `` similar '' k nodes to a given query node . One widely used class of similarity measures is based on random walks on graphs , e.g. , personalized pagerank , hitting and commute times , and simrank . There are two fundamental problems associated with these measures . First , existing online algorithms typically examine the local neighborhood of the query node which can become significantly slower whenever high-degree nodes are encountered ( a common phenomenon in real-world graphs ) . We prove that turning high degree nodes into sinks results in only a small approximation error , while greatly improving running times . The second problem is that of computing similarities at query time when the graph is too large to be memory-resident . The obvious solution is to split the graph into clusters of nodes and store each cluster on a disk page ; ideally random walks will rarely cross cluster boundaries and cause page-faults . Our contributions here are twofold : ( a ) we present an efficient deterministic algorithm to find the k closest neighbors ( in terms of personalized pagerank ) of any query node in such a clustered graph , and ( b ) we develop a clustering algorithm ( RWDISK ) that uses only sequential sweeps over data files . Empirical results on several large publicly available graphs like DBLP , Citeseer and Live-Journal ( ~ 90 M edges ) demonstrate that turning high degree nodes into sinks not only improves running time of RWDISK by a factor of 3 but also boosts link prediction accuracy by a factor of 4 on average . We also show that RWDISK returns more desirable ( high conductance and small size ) clusters than the popular clustering algorithm METIS , while requiring much less memory . Finally our deterministic algorithm for computing nearest neighbors incurs far fewer page-faults ( factor of 5 ) than actually simulating random walks .

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


Designing efficient cascaded classifiers : tradeoff between accuracy and cost We propose a method to train a cascade of classifiers by simultaneously optimizing all its stages . The approach relies on the idea of optimizing soft cascades . In particular , instead of optimizing a deterministic hard cascade , we optimize a stochastic soft cascade where each stage accepts or rejects samples according to a probability distribution induced by the previous stage-specific classifier . The overall system accuracy is maximized while explicitly controlling the expected cost for feature acquisition . Experimental results on three clinically relevant problems show the effectiveness of our proposed approach in achieving the desired tradeoff between accuracy and feature acquisition cost .

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

accuracy vs cost
cascade design
cost sensitive learning


A new multi-view regression approach with an application to customer wallet estimation Motivated by the problem of customer wallet estimation , we propose a new setting for multi-view regression , where we learn a completely unobserved target ( in our case , customer wallet ) by modeling it as a `` central link '' in a directed graphical model , connecting multiple sets of observed variables . The resulting conditional independence allows us to reduce the maximum discriminative likelihood estimation problem to a convex optimization problem for exponential linear models . We show that under certain modeling assumptions , in particular , when there exist two conditionally independent views and the noise is Gaussian , this problem can be reduced to a single least squares regression . Thus , for this specific , but widely applicable setting , the `` unsupervised '' multi-view problem can be solved via a simple supervised learning approach . This reduction also allows us to test the statistical independence assumptions underlying the graphical model and perform variable selection . We demonstrate the effectiveness of our approach on our motivating problem of customer wallet estimation and on simulation data .

O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O 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 generalized Co-HITS algorithm and its application to bipartite graphs Recently many data types arising from data mining and Web search applications can be modeled as bipartite graphs . Examples include queries and URLs in query logs , and authors and papers in scientific literature . However , one of the issues is that previous algorithms only consider the content and link information from one side of the bipartite graph . There is a lack of constraints to make sure the final relevance of the score propagation on the graph , as there are many noisy edges within the bipartite graph . In this paper , we propose a novel and general Co-HITS algorithm to incorporate the bipartite graph with the content information from both sides as well as the constraints of relevance . Moreover , we investigate the algorithm based on two frameworks , including the iterative and the regularization frameworks , and illustrate the generalized Co-HITS algorithm from different views . For the iterative framework , it contains HITS and personalized PageRank as special cases . In the regularization framework , we successfully build a connection with HITS , and develop a new cost function to consider the direct relationship between two entity sets , which leads to a significant improvement over the baseline method . To illustrate our methodology , we apply the Co-HITS algorithm , with many different settings , to the application of query suggestion by mining the AOL query log data . Experimental results demonstrate that CoRegu-0 .5 ( i.e. , a model of the regularization framework ) achieves the best performance with consistent and promising improvements .

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

bipartite graphs
regularization
score propagation


Relational data pre-processing techniques for improved securities fraud detection Commercial datasets are often large , relational , and dynamic . They contain many records of people , places , things , events and their interactions over time . Such datasets are rarely structured appropriately for knowledge discovery , and they often contain variables whose meanings change across different subsets of the data . We describe how these challenges were addressed in a collaborative analysis project undertaken by the University of Massachusetts Amherst and the National Association of Securities Dealers ( NASD ) . We describe several methods for data pre-processing that we applied to transform a large , dynamic , and relational dataset describing nearly the entirety of the U.S. securities industry , and we show how these methods made the dataset suitable for learning statistical relational models . To better utilize social structure , we first applied known consolidation and link formation techniques to associate individuals with branch office locations . In addition , we developed an innovative technique to infer professional associations by exploiting dynamic employment histories . Finally , we applied normalization techniques to create a suitable class label that adjusts for spatial , temporal , and other heterogeneity within the data . We show how these pre-processing techniques combine to provide the necessary foundation for learning high-performing statistical models of fraudulent activity .

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 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

data pre-processing
relational probability trees
statistical relational learning


Weighting versus pruning in rule validation for detecting network and host anomalies For intrusion detection , the LERAD algorithm learns a succinct set of comprehensible rules for detecting anomalies , which could be novel attacks . LERAD validates the learned rules on a separate held-out validation set and removes rules that cause false alarms . However , removing rules with possible high coverage can lead to missed detections . We propose to retain these rules and associate weights to them . We present three weighting schemes and our empirical results indicate that , for LERAD , rule weighting can detect more attacks than pruning with minimal computational overhead .

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

anomaly detection
invasive software
machine learning
rule pruning
unauthorized access


Sequential cost-sensitive decision making with reinforcement learning Recently , there has been increasing interest in the issues of cost-sensitive learning and decision making in a variety of applications of data mining . A number of approaches have been developed that are effective at optimizing cost-sensitive decisions when each decision is considered in isolation . However , the issue of sequential decision making , with the goal of maximizing total benefits accrued over a period of time instead of immediate benefits , has rarely been addressed . In the present paper , we propose a novel approach to sequential decision making based on the reinforcement learning framework . Our approach attempts to learn decision rules that optimize a sequence of cost-sensitive decisions so as to maximize the total benefits accrued over time . We use the domain of targeted ' marketing as a testbed for empirical evaluation of the proposed method . We conducted experiments using approximately two years of monthly promotion data derived from the well-known KDD Cup 1998 donation data set . The experimental results show that the proposed method for optimizing total accrued benefits out performs the usual targeted-marketing methodology of optimizing each promotion in isolation . We also analyze the behavior of the targeting rules that were obtained and discuss their appropriateness to the application domain .

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


Eigenspace-based anomaly detection in computer systems We report on an automated runtime anomaly detection method at the application layer of multi-node computer systems . Although several network management systems are available in the market , none of them have sufficient capabilities to detect faults in multi-tier Web-based systems with redundancy . We model a Web-based system as a weighted graph , where each node represents a `` service '' and each edge represents a dependency between services . Since the edge weights vary greatly over time , the problem we address is that of anomaly detection from a time sequence of graphs . In our method , we first extract a feature vector from the adjacency matrix that represents the activities of all of the services . The heart of our method is to use the principal eigenvector of the eigenclusters of the graph . Then we derive a probability distribution for an anomaly measure defined for a time-series of directional data derived from the graph sequence . Given a critical probability , the threshold value is adaptively updated using a novel online algorithm . We demonstrate that a fault in a Web application can be automatically detected and the faulty services are identified without using detailed knowledge of the behavior of the system .

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

principal eigenvector
time sequence of graphs

learning
perron-frobenius theorem
singular value decomposition
von mises-fisher distribution


Combining email models for false positive reduction Machine learning and data mining can be effectively used to model , classify and discover interesting information for a wide variety of data including email . The Email Mining Toolkit , EMT , has been designed to provide a wide range of analyses for arbitrary email sources . Depending upon the task , one can usually achieve very high accuracy , but with some amount of false positive tradeoff . Generally false positives are prohibitively expensive in the real world . In the case of spam detection , for example , even if one email is misclassified , this may be unacceptable if it is a very important email . Much work has been done to improve specific algorithms for the task of detecting unwanted messages , but less work has been report on leveraging multiple algorithms and correlating models in this particular domain of email analysis . EMT has been updated with new correlation functions allowing the analyst to integrate a number of EMT 's user behavior models available in the core technology . We present results of combining classifier outputs for improving both accuracy and reducing false positives for the problem of spam detection . We apply these methods to a very large email data set and show results of different combination methods on these corpora . We introduce a new method to compare multiple and combined classifiers , and show how it differs from past work . The method analyzes the relative gain and maximum possible accuracy that can be achieved for certain combinations of classifiers to automatically choose the best combination .

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

data mining
email mining
false positive reduction
spam

aggregators
model combination
multiple classifiers


Extracting key-substring-group features for text classification In many text classification applications , it is appealing to take every document as a string of characters rather than a bag of words . Previous research studies in this area mostly focused on different variants of generative Markov chain models . Although discriminative machine learning methods like Support Vector Machine ( SVM ) have been quite successful in text classification with word features , it is neither effective nor efficient to apply them straightforwardly taking all substrings in the corpus as features . In this paper , we propose to partition all substrings into statistical equivalence groups , and then pick those groups which are important ( in the statistical sense ) as features ( named key-substring-group features ) for text classification . In particular , we propose a suffix tree based algorithm that can extract such features in linear time ( with respect to the total number of characters in the corpus ) . Our experiments on English , Chinese and Greek datasets show that SVM with key-substring-group features can achieve outstanding performance for various text classification tasks .

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

machine learning
suffix tree
text classification

content analysis and indexing
feature extraction
text mining


Discovering informative content blocks from Web documents In this paper , we propose a new approach to discover informative contents from a set of tabular documents ( or Web pages ) of a Web site . Our system , InfoDiscoverer , first partitions a page into several content blocks according to HTML tag ( TABLE ) in a Web page . Based on the occurrence of the features ( terms ) in the set of pages , it calculates entropy value of each feature . According to the entropy value of each feature in a content block , the entropy value of the block is defined . By analyzing the information measure , we propose a method to dynamically select the entropy-threshold that partitions blocks into either informative or redundant . Informative content blocks are distinguished parts of the page , whereas redundant content blocks are common parts . Based on the answer set generated from 13 manually tagged news Web sites with a total of 26,518 Web pages , experiments show that both recall and precision rates are greater than 0.956 . That is , using the approach , informative blocks ( news articles ) of these sites can be automatically separated from semantically redundant contents such as advertisements , banners , navigation panels , news categories , etc. . By adopting InfoDiscoverer as the preprocessor of information retrieval and extraction applications , the retrieval and extracting precision will be increased , and the indexing size and extracting complexity will also be reduced .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O 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 B I O 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 extraction
informative content discovery


Fast nonlinear regression via eigenimages applied to galactic morphology Astronomy increasingly faces the issue of massive , unwieldly data sets . The Sloan Digital Sky Survey ( SDSS ) ( 11 ) has so far generated tens of millions of images of distant galaxies , of which only a tiny fraction have been morphologically classified . Morphological classification in this context is achieved by fitting a parametric model of galaxy shape to a galaxy image . This is a nonlinear regression problem , whose challenges are threefold , 1 ) blurring of the image caused by atmosphere and mirror imperfections , 2 ) large numbers of local minima , and 3 ) massive data sets . Our strategy is to use the eigenimages of the parametric model to form a new feature space , and then to map both target image and the model parameters into this feature space . In this low-dimensional space we search for the best image-to-parameter match . To search the space , we sample it by creating a database of many random parameter vectors ( prototypes ) and mapping them into the feature space . The search problem then becomes one of finding the best prototype match , so the fitting process a nearest-neighbor search . In addition to the savings realized by decomposing the original space into an eigenspace , we can use the fact that the model is a linear sum of functions to reduce the prototypes further : the only prototypes stored are the components of the model function . A modified form of nearest neighbor is used to search among them . Additional complications arise in the form of missing data and heteroscedasticity , both of which are addressed with weighted linear regression . Compared to existing techniques , speed-ups ach-ieved are between 2 and 3 orders of magnitude . This should enable the analysis of the entire SDSS dataset .

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

astronomy
morphology
nearest neighbor
regression

implementation
pincipal component analysis


Latent aspect rating analysis on review text data : a rating regression approach In this paper , we define and study a new opinionated text data analysis problem called Latent Aspect Rating Analysis ( LARA ) , which aims at analyzing opinions expressed about an entity in an online review at the level of topical aspects to discover each individual reviewer 's latent opinion on each aspect as well as the relative emphasis on different aspects when forming the overall judgment of the entity . We propose a novel probabilistic rating regression model to solve this new text mining problem in a general way . Empirical experiments on a hotel review data set show that the proposed latent rating regression model can effectively solve the problem of LARA , and that the detailed analysis of opinions at the level of topical aspects enabled by the proposed model can support a wide range of application tasks , such as aspect opinion summarization , entity ranking based on aspect ratings , and analysis of reviewers rating behavior .

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


Extending naïve Bayes classifiers using long itemsets

association mining
bayesian learning
classification
lazy learning


Simultaneous record detection and attribute labeling in web data extraction Recent work has shown the feasibility and promise of template-independent Web data extraction . However , existing approaches use decoupled strategies - attempting to do data record detection and attribute labeling in two separate phases . In this paper , we show that separately extracting data records and attributes is highly ineffective and propose a probabilistic model to perform these two tasks simultaneously . In our approach , record detection can benefit from the availability of semantics required in attribute labeling and , at the same time , the accuracy of attribute labeling can be improved when data records are labeled in a collective manner . The proposed model is called Hierarchical Conditional Random Fields . It can efficiently integrate all useful features by learning their importance , and it can also incorporate hierarchical interactions which are very important for Web data extraction . We empirically compare the proposed model with existing decoupled approaches for product information extraction , and the results show significant improvements in both record detection and attribute labeling .

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 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 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 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 B I O

attribute labeling
conditional random fields
data record detection

hierarchical conditional random fields
web page segmentation


Deriving marketing intelligence from online discussion Weblogs and message boards provide online forums for discussion that record the voice of the public . Woven into this mass of discussion is a wide range of opinion and commentary about consumer products . This presents an opportunity for companies to understand and respond to the consumer by analyzing this unsolicited feedback . Given the volume , format and content of the data , the appropriate approach to understand this data is to use large-scale web and text data mining technologies . This paper argues that applications for mining large volumes of textual data for marketing intelligence should provide two key elements : a suite of powerful mining and visualization technologies and an interactive analysis environment which allows for rapid generation and testing of hypotheses . This paper presents such a system that gathers and annotates online discussion relating to consumer products using a wide variety of state-of-the-art techniques , including crawling , wrapping , search , text classification and computational linguistics . Marketing intelligence is derived through an interactive analysis framework uniquely configured to leverage the connectivity and content of annotated online discussion .

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

content systems
information retrieval
information search and retrieval
machine learning
text mining


Disease progression modeling from historical clinical databases This paper considers the problem of modeling disease progression from historical clinical databases , with the ultimate objective of stratifying patients into groups with clearly distinguishable prognoses or suitability for different treatment strategies . To meet this objective , we describe a procedure that first fits clinical variables measured over time to a disease progression model . The resulting parameter estimates are then used as the basis for a stepwise clustering procedure to stratify patients into groups with distinct survival characteristics . As a practical illustration , we apply this procedure to survival prediction , using a liver transplant database from the National Institute of Diabetes and Digestive and Kidney Diseases ( NIDDK ) .

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

censoring
cluster analysis
logistic model
model development
niddk liver transplant database


Differentially private recommender systems : building privacy into the net We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users . Specifically , we consider the Netflix Prize data set , and its leading algorithms , adapted to the framework of differential privacy . Unlike prior privacy work concerned with cryptographically securing the computation of recommendations , differential privacy constrains a computation in a way that precludes any inference about the underlying records from its output . Such algorithms necessarily introduce uncertainty -- i.e. , noise -- to computations , trading accuracy for privacy . We find that several of the leading approaches in the Netflix Prize competition can be adapted to provide differential privacy , without significantly degrading their accuracy . To adapt these algorithms , we explicitly factor them into two parts , an aggregation\/learning phase that can be performed with differential privacy guarantees , and an individual recommendation phase that uses the learned correlations and an individual 's data to provide personalized recommendations . The adaptations are non-trivial , and involve both careful analysis of the per-record sensitivity of the algorithms to calibrate noise , as well as new post-processing steps to mitigate the impact of this noise . We measure the empirical trade-off between accuracy and privacy in these adaptations , and find that we can provide non-trivial formal privacy guarantees while still outperforming the Cinematch baseline Netflix provides .

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

differential privacy
netflix
recommender systems


Variable latent semantic indexing Latent Semantic Indexing is a classical method to produce optimal low-rank approximations of a term-document matrix . However , in the context of a particular query distribution , the approximation thus produced need not be optimal . We propose VLSI , a new query-dependent ( or `` variable '' ) low-rank approximation that minimizes approximation error for any specified query distribution . With this tool , it is possible to tailor the LSI technique to particular settings , often resulting in vastly improved approximations at much lower dimensionality . We validate this method via a series of experiments on classical corpora , showing that VLSI typically performs similarly to LSI with an order of magnitude fewer dimensions .

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

sparse, structured, and very large systems


An objective evaluation criterion for clustering We propose and test an objective criterion for evaluation of clustering performance : How well does a clustering algorithm run on unlabeled data aid a classification algorithm ? The accuracy is quantified using the PAC-MDL bound ( 3 ) in a semisupervised setting . Clustering algorithms which naturally separate the data according to ( hidden ) labels with a small number of clusters perform well . A simple extension of the argument leads to an objective model selection method . Experimental results on text analysis datasets demonstrate that this approach empirically results in very competitive bounds on test set performance on natural datasets .

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


Efficiently handling feature redundancy in high-dimensional data High-dimensional data poses a severe challenge for data mining . Feature selection is a frequently used technique in pre-processing high-dimensional data for successful data mining . Traditionally , feature selection is focused on removing irrelevant features . However , for high-dimensional data , removing redundant features is equally critical . In this paper , we provide a study of feature redundancy in high-dimensional data and propose a novel correlation-based approach to feature selection within the filter model . The extensive empirical study using real-world data shows that the proposed approach is efficient and effective in removing redundant and irrelevant features .

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


Proximal support vector machine classifiers Instead of a standard support vector machine ( SVM ) that classifies points by assigning them to one of two disjoint half-spaces , points are classified by assigning them to the closest of two parallel planes ( in input or feature space ) that are pushed apart as far as possible . This formulation , which can also be interpreted as regularized least squares and considered in the much more general context of regularized networks ( 8 , 9 ) , leads to an extremely fast and simple algorithm for generating a linear or nonlinear classifier that merely requires the solution of a single system of linear equations . In contrast , standard SVMs solve a quadratic or a linear program that require considerably longer computational time . Computational results on publicly available datasets indicate that the proposed proximal SVM classifier has comparable test set correctness to that of standard SVM classifiers , but with considerably faster computational time that can be an order of magnitude faster . The linear proximal SVM can easily handle large datasets as indicated by the classification of a 2 million point 10-attribute set in 20.8 seconds . All computational results are based on 6 lines of MATLAB code .

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

data classification
support vector machines


PEBL : positive example based learning for Web page classification using SVM Web page classification is one of the essential techniques for Web mining . Specifically , classifying Web pages of a user-interesting class is the first step of mining interesting information from the Web . However , constructing a classifier for an interesting class requires laborious pre-processing such as collecting positive and negative training examples . For instance , in order to construct a `` homepage '' classifier , one needs to collect a sample of homepages ( positive examples ) and a sample of non-homepages ( negative examples ) . In particular , collecting negative training examples requires arduous work and special caution to avoid biasing them . We introduce in this paper the Positive Example Based Learning ( PEBL ) framework for Web page classification which eliminates the need for manually collecting negative training examples in pre-processing . We present an algorithm called Mapping-Convergence ( M-C ) that achieves classification accuracy ( with positive and unlabeled data ) as high as that of traditional SVM ( with positive and negative data ) . Our experiments show that when the M-C algorithm uses the same amount of positive examples as that of traditional SVM , the M-C algorithm performs as well as traditional SVM .

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

labeled data
mapping-convergence algorithm


Transfer metric learning by learning task relationships Distance metric learning plays a very crucial role in many data mining algorithms because the performance of an algorithm relies heavily on choosing a good metric . However , the labeled data available in many applications is scarce and hence the metrics learned are often unsatisfactory . In this paper , we consider a transfer learning setting in which some related source tasks with labeled data are available to help the learning of the target task . We first propose a convex formulation for multi-task metric learning by modeling the task relationships in the form of a task covariance matrix . Then we regard transfer learning as a special case of multi-task learning and adapt the formulation of multi-task metric learning to the transfer learning setting for our method , called transfer metric learning ( TML ) . In TML , we learn the metric and the task covariances between the source tasks and the target task under a unified convex formulation . To solve the convex optimization problem , we use an alternating method in which each subproblem has an efficient solution . Experimental results on some commonly used transfer learning applications demonstrate the effectiveness of our method .

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

metric learning
multi-task learning
transfer learning


Mining frequent item sets by opportunistic projection In this paper , we present a novel algorithm Opportune Project for mining complete set of frequent item sets by projecting databases to grow a frequent item set tree . Our algorithm is fundamentally different from those proposed in the past in that it opportunistically chooses between two different structures , array-based or tree-based , to represent projected transaction subsets , and heuristically decides to build unfiltered pseudo projection or to make a filtered copy according to features of the subsets . More importantly , we propose novel methods to build tree-based pseudo projections and array-based unfiltered projections for projected transaction subsets , which makes our algorithm both CPU time efficient and memory saving . Basically , the algorithm grows the frequent item set tree by depth first search , whereas breadth first search is used to build the upper portion of the tree if necessary . We test our algorithm versus several other algorithms on real world datasets , such as BMS-POS , and on IBM artificial datasets . The empirical results show that our algorithm is not only the most efficient on both sparse and dense databases at all levels of support threshold , but also highly scalable to very large 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O