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IEEE Transactions on Knowledge and Data Engineering

Issue 4 • Date April 2010

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Displaying Results 1 - 16 of 16
  • [Front cover]

    Publication Year: 2010, Page(s): c1
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  • [Inside front cover]

    Publication Year: 2010, Page(s): c2
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  • A Game Theoretic Approach for Simultaneous Compaction and Equipartitioning of Spatial Data Sets

    Publication Year: 2010, Page(s):465 - 478
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3280 KB) | HTML iconHTML

    Data and object clustering techniques are used in a wide variety of scientific applications such as biology, pattern recognition, information systems, etc. Traditionally, clustering methods have focused on optimizing a single metric, however, several multidisciplinary applications such as robot team deployment, ad hoc networks, facility location, etc., require the simultaneous examination of multi... View full abstract»

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  • A Nonsupervised Learning Framework of Human Behavior Patterns Based on Sequential Actions

    Publication Year: 2010, Page(s):479 - 492
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5047 KB) | HTML iconHTML

    In designing autonomous service systems such as assistive robots for the aged and the disabled, discovery and prediction of human actions are important and often crucial. Patterns of human behavior, however, involve ambiguity, uncertainty, complexity, and inconsistency caused by physical, logical, and emotional factors, and thus their modeling and recognition are known to be difficult. In this pap... View full abstract»

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  • Duplicate-Insensitive Order Statistics Computation over Data Streams

    Publication Year: 2010, Page(s):493 - 507
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1868 KB) | HTML iconHTML

    Duplicates in data streams may often be observed by the projection on a subspace and/or multiple recordings of objects. Without the uniqueness assumption on observed data elements, many conventional aggregates computation problems need to be further investigated due to their duplication-sensitive nature. In this paper, we present novel, space-efficient, one-scan algorithms to continuously maintain... View full abstract»

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  • Filter-Based Data Partitioning for Training Multiple Classifier Systems

    Publication Year: 2010, Page(s):508 - 522
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3615 KB) | HTML iconHTML

    Data partitioning methods such as bagging and boosting have been extensively used in multiple classifier systems. These methods have shown a great potential for improving classification accuracy. This study is concerned with the analysis of training data distribution and its impact on the performance of multiple classifier systems. In this study, several feature-based and class-based measures are ... View full abstract»

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  • Learning to Adapt Web Information Extraction Knowledge and Discovering New Attributes via a Bayesian Approach

    Publication Year: 2010, Page(s):523 - 536
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3347 KB) | HTML iconHTML

    This paper presents a Bayesian learning framework for adapting information extraction wrappers with new attribute discovery, reducing human effort in extracting precise information from unseen Web sites. Our approach aims at automatically adapting the information extraction knowledge previously learned from a source Web site to a new unseen site, at the same time, discovering previously unseen att... View full abstract»

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  • Efficient Algorithm for Localized Support Vector Machine

    Publication Year: 2010, Page(s):537 - 549
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3029 KB) | HTML iconHTML

    This paper presents a framework called localized support vector machine (LSVM) for classifying data with nonlinear decision surfaces. Instead of building a sophisticated global model from the training data, LSVM constructs multiple linear SVMs, each of which is designed to accurately classify a given test example. A major limitation of this framework is its high computational cost since a unique m... View full abstract»

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  • Probabilistic Reverse Nearest Neighbor Queries on Uncertain Data

    Publication Year: 2010, Page(s):550 - 564
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2561 KB) | HTML iconHTML

    Uncertain data are inherent in various important applications and reverse nearest neighbor (RNN) query is an important query type for many applications. While many different types of queries have been studied on uncertain data, there is no previous work on answering RNN queries on uncertain data. In this paper, we formalize probabilistic reverse nearest neighbor query that is to retrieve the objec... View full abstract»

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  • Prospective Infectious Disease Outbreak Detection Using Markov Switching Models

    Publication Year: 2010, Page(s):565 - 577
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2121 KB) | HTML iconHTML Multimedia Media

    Accurate and timely detection of infectious disease outbreaks provides valuable information which can enable public health officials to respond to major public health threats in a timely fashion. However, disease outbreaks are often not directly observable. For surveillance systems used to detect outbreaks, noises caused by routine behavioral patterns and by special events can further complicate t... View full abstract»

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  • Record Matching over Query Results from Multiple Web Databases

    Publication Year: 2010, Page(s):578 - 589
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2432 KB) | HTML iconHTML

    Record matching, which identifies the records that represent the same real-world entity, is an important step for data integration. Most state-of-the-art record matching methods are supervised, which requires the user to provide training data. These methods are not applicable for the Web database scenario, where the records to match are query results dynamically generated on-the-fly. Such records ... View full abstract»

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  • The Tiled Bitmap Forensic Analysis Algorithm

    Publication Year: 2010, Page(s):590 - 601
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2783 KB) | HTML iconHTML Multimedia Media

    Tampering of a database can be detected through the use of cryptographically strong hash functions. Subsequently, applied forensic analysis algorithms can help determine when, what, and perhaps ultimately who and why. This paper presents a novel forensic analysis algorithm, the tiled Bitmap algorithm, which is more efficient than prior forensic analysis algorithms. It introduces the notion of a ca... View full abstract»

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  • A Binary String Approach for Updates in Dynamic Ordered XML Data

    Publication Year: 2010, Page(s):602 - 607
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (893 KB) | HTML iconHTML

    To facilitate XML query processing, several labeling schemes have been proposed, in which the ancestor-descendant and parent-child relationships in XML queries can be quickly determined without accessing the original XML file. However, all of these existing schemes have to relabel the existing nodes or recalculate certain values when order-sensitive updates cause insertions, thus causing the label... View full abstract»

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  • 7 Great Reasons for Joining the IEEE Computer Society [advertisement]

    Publication Year: 2010, Page(s): 608
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  • TKDE Information for authors

    Publication Year: 2010, Page(s): c3
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  • [Back cover]

    Publication Year: 2010, Page(s): c4
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Aims & Scope

IEEE Transactions on Knowledge and Data Engineering (TKDE) informs researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Jian Pei
Simon Fraser University

Associate Editor-in-Chief
Xuemin Lin
University of New South Wales

Associate Editor-in-Chief
Lei Chen
Hong Kong University of Science and Technology