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IEEE Transactions on Knowledge and Data Engineering

Issue 6 • Date June 2010

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Displaying Results 1 - 19 of 19
  • [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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  • Introduction to the Domain-Drive Data Mining Special Section

    Publication Year: 2010, Page(s):753 - 754
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  • Domain-Driven Data Mining: Challenges and Prospects

    Publication Year: 2010, Page(s):755 - 769
    Cited by:  Papers (20)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1450 KB) | HTML iconHTML

    Traditional data mining research mainly focus]es on developing, demonstrating, and pushing the use of specific algorithms and models. The process of data mining stops at pattern identification. Consequently, a widely seen fact is that 1) many algorithms have been designed of which very few are repeatable and executable in the real world, 2) often many patterns are mined but a major proportion of t... View full abstract»

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  • Bridging Domains Using World Wide Knowledge for Transfer Learning

    Publication Year: 2010, Page(s):770 - 783
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5238 KB) | HTML iconHTML

    A major problem of classification learning is the lack of ground-truth labeled data. It is usually expensive to label new data instances for training a model. To solve this problem, domain adaptation in transfer learning has been proposed to classify target domain data by using some other source domain data, even when the data may have different distributions. However, domain adaptation may not wo... View full abstract»

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  • Knowledge-Based Interactive Postmining of Association Rules Using Ontologies

    Publication Year: 2010, Page(s):784 - 797
    Cited by:  Papers (26)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2077 KB) | HTML iconHTML

    In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. To overcome this drawback, several methods were proposed in the literature such as itemset concise representations, redundancy reduction, and postprocessing. However, being generally based on statistical information, most of these methods do not guarantee that the extracted rules are inte... View full abstract»

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  • Logic-Based Pattern Discovery

    Publication Year: 2010, Page(s):798 - 811
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2247 KB) | HTML iconHTML

    In the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. The accuracy in setting up this threshold directly influences the number and the quality of association rules discovered. Often, the number of association rules, even though large in number, misses some interesting rules and the rules' quality necessitates further analysis. ... View full abstract»

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  • Asking Generalized Queries to Domain Experts to Improve Learning

    Publication Year: 2010, Page(s):812 - 825
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3049 KB) | HTML iconHTML

    With the assistance of a domain expert, active learning can often select or construct fewer examples to request their labels to build an accurate classifier. However, previous works of active learning can only generate and ask specific queries. In real-world applications, the domain experts (or oracles) are often more readily to answer ??generalized queries?? with don't-care attributes. The power ... View full abstract»

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  • Domain-Driven Classification Based on Multiple Criteria and Multiple Constraint-Level Programming for Intelligent Credit Scoring

    Publication Year: 2010, Page(s):826 - 838
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2216 KB) | HTML iconHTML

    Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, becau... View full abstract»

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  • Signaling Potential Adverse Drug Reactions from Administrative Health Databases

    Publication Year: 2010, Page(s):839 - 853
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3132 KB) | HTML iconHTML

    The work is motivated by real-world applications of detecting Adverse Drug Reactions (ADRs) from administrative health databases. ADRs are a leading cause of hospitalization and death worldwide. Almost all current postmarket ADR signaling techniques are based on spontaneous ADR case reports, which suffer from serious underreporting and latency. However, administrative health data are widely and ro... View full abstract»

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  • Feature Selection Using f-Information Measures in Fuzzy Approximation Spaces

    Publication Year: 2010, Page(s):854 - 867
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (7929 KB) | HTML iconHTML

    The selection of nonredundant and relevant features of real-valued data sets is a highly challenging problem. A novel feature selection method is presented here based on fuzzy-rough sets by maximizing the relevance and minimizing the redundancy of the selected features. By introducing the fuzzy equivalence partition matrix, a novel representation of Shannon's entropy for fuzzy approximation spaces... View full abstract»

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  • δ-Presence without Complete World Knowledge

    Publication Year: 2010, Page(s):868 - 883
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3576 KB) | HTML iconHTML Multimedia Media

    Advances in information technology, and its use in research, are increasing both the need for anonymized data and the risks of poor anonymization. We presented a new privacy metric, δ-presence, that clearly links the quality of anonymization to the risk posed by inadequate anonymization. It was shown that existing anonymization techniques are inappropriate for situations where δ-pr... View full abstract»

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  • Privacy-Preserving Gradient-Descent Methods

    Publication Year: 2010, Page(s):884 - 899
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1810 KB) | HTML iconHTML

    Gradient descent is a widely used paradigm for solving many optimization problems. Gradient descent aims to minimize a target function in order to reach a local minimum. In machine learning or data mining, this function corresponds to a decision model that is to be discovered. In this paper, we propose a preliminary formulation of gradient descent with data privacy preservation. We present two app... View full abstract»

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  • Dynamic Dissimilarity Measure for Support-Based Clustering

    Publication Year: 2010, Page(s):900 - 905
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2185 KB) | HTML iconHTML

    Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure... View full abstract»

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  • Kernel Discriminant Learning for Ordinal Regression

    Publication Year: 2010, Page(s):906 - 910
    Cited by:  Papers (43)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1039 KB) | HTML iconHTML

    Ordinal regression has wide applications in many domains where the human evaluation plays a major role. Most current ordinal regression methods are based on Support Vector Machines (SVM) and suffer from the problems of ignoring the global information of the data and the high computational complexity. Linear Discriminant Analysis (LDA) and its kernel version, Kernel Discriminant Analysis (KDA), tak... View full abstract»

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  • IEEE and IEEE Computer Society 2010 Student Package

    Publication Year: 2010, Page(s): 911
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  • 7 Great Reasons for Joining the IEEE Computer Society [advertisement]

    Publication Year: 2010, Page(s): 912
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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.

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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