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IEEE Transactions on Knowledge and Data Engineering

Issue 4 • Date April 2006

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Displaying Results 1 - 17 of 17
  • [Front cover]

    Publication Year: 2006, Page(s): c1
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  • [Inside front cover]

    Publication Year: 2006, Page(s): c2
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  • q-gram matching using tree models

    Publication Year: 2006, Page(s):433 - 447
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2904 KB) | HTML iconHTML

    q-gram matching is used for approximate substring matching problems in a wide range of application areas, including intrusion detection. In this paper, we present a tree-based model to perform fast linear time q-gram matching. All q-grams present in the text are stored in a tree structure similar to trie. We use a tree redundancy pruning algorithm to reduce the size of the tree without losing any ... View full abstract»

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  • Multitype features coselection for Web document clustering

    Publication Year: 2006, Page(s):448 - 459
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3368 KB) | HTML iconHTML

    Feature selection has been widely applied in text categorization and clustering. Compared to unsupervised selection, supervised feature selection is more successful in filtering out noise in most cases. However, due to a lack of label information, clustering can hardly exploit supervised selection. Some studies have proposed to solve this problem by "pseudoclass." As empirical results show, this m... View full abstract»

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  • On optimal rule discovery

    Publication Year: 2006, Page(s):460 - 471
    Cited by:  Papers (29)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1328 KB) | HTML iconHTML

    In machine learning and data mining, heuristic and association rules are two dominant schemes for rule discovery. Heuristic rule discovery usually produces a small set of accurate rules, but fails to find many globally optimal rules. Association rule discovery generates all rules satisfying some constraints, but yields too many rules and is infeasible when the minimum support is small. Here, we pr... View full abstract»

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  • A transaction mapping algorithm for frequent itemsets mining

    Publication Year: 2006, Page(s):472 - 481
    Cited by:  Papers (32)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2056 KB) | HTML iconHTML

    In this paper, we present a novel algorithm for mining complete frequent itemsets. This algorithm is referred to as the TM (transaction mapping) algorithm from hereon. In this algorithm, transaction ids of each itemset are mapped and compressed to continuous transaction intervals in a different space and the counting of itemsets is performed by intersecting these interval lists in a depth-first or... View full abstract»

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  • A unifying framework for detecting outliers and change points from time series

    Publication Year: 2006, Page(s):482 - 492
    Cited by:  Papers (69)  |  Patents (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2056 KB) | HTML iconHTML

    We are concerned with the issue of detecting outliers and change points from time series. In the area of data mining, there have been increased interest in these issues since outlier detection is related to fraud detection, rare event discovery, etc., while change-point detection is related to event/trend change detection, activity monitoring, etc. Although, in most previous work, outlier detectio... View full abstract»

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  • TAPER: a two-step approach for all-strong-pairs correlation query in large databases

    Publication Year: 2006, Page(s):493 - 508
    Cited by:  Papers (10)  |  Patents (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3720 KB) | HTML iconHTML

    Given a user-specified minimum correlation threshold θ and a market-basket database with N items and T transactions, an all-strong-pairs correlation query finds all item pairs with correlations above the threshold θ. However, when the number of items and transactions are large, the computation cost of this query can be very high. The goal of this paper is to provide computationally eff... View full abstract»

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  • A unified log-based relevance feedback scheme for image retrieval

    Publication Year: 2006, Page(s):509 - 524
    Cited by:  Papers (55)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3600 KB) | HTML iconHTML

    Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be abl... View full abstract»

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  • Keyword proximity search in XML trees

    Publication Year: 2006, Page(s):525 - 539
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3920 KB) | HTML iconHTML

    Recent works have shown the benefits of keyword proximity search in querying XML documents in addition to text documents. For example, given query keywords over Shakespeare's plays in XML, the user might be interested in knowing how the keywords cooccur. In this paper, we focus on XML trees and define XML keyword, proximity queries to return the (possibly heterogeneous) set of minimum connecting t... View full abstract»

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  • Reverse nearest neighbors in large graphs

    Publication Year: 2006, Page(s):540 - 553
    Cited by:  Papers (24)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3800 KB) | HTML iconHTML

    A reverse nearest neighbor (RNN) query returns the data objects that have a query point as their nearest neighbor (NN). Although such queries have been studied quite extensively in Euclidean spaces, there is no previous work in the context of large graphs. In this paper, we provide a fundamental lemma, which can be used to prune the search space while traversing the graph in search for RNN. Based ... View full abstract»

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  • Mining ontology for automatically acquiring Web user information needs

    Publication Year: 2006, Page(s):554 - 568
    Cited by:  Papers (56)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2904 KB) | HTML iconHTML

    It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data s... View full abstract»

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  • On consistent reading of entire databases

    Publication Year: 2006, Page(s):569 - 572
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (488 KB) | HTML iconHTML

    Many applications need to read an entire database in a consistent way. This global-reading of an entire database formulated as a global-read transaction (GRT) is not a trivial issue since it will cause a high degree of interference to other concurrent transactions. Conventional concurrency control protocols are obviously inadequate in handling the long-lived GRT. Previous studies proposed addition... View full abstract»

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  • Call for Papers for Special Issue on Knowledge and Data Management and Engineering in Intelligence and Security Informatics

    Publication Year: 2006, Page(s): 573
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  • [Advertisement]

    Publication Year: 2006, Page(s):574 - 576
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  • TKDE Information for authors

    Publication Year: 2006, Page(s): c3
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  • [Back cover]

    Publication Year: 2006, 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