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IEEE Transactions on Knowledge and Data Engineering

Issue 3 • Date March 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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  • A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction

    Publication Year: 2010, Page(s):305 - 317
    Cited by:  Papers (39)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2688 KB) | HTML iconHTML

    Feature Selection (FS) or Attribute Reduction techniques are employed for dimensionality reduction and aim to select a subset of the original features of a data set which are rich in the most useful information. The benefits of employing FS techniques include improved data visualization and transparency, a reduction in training and utilization times and potentially, improved prediction performance... View full abstract»

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  • A General Framework of Time-Variant Bandwidth Allocation in the Data Broadcasting Environment

    Publication Year: 2010, Page(s):318 - 333
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2610 KB) | HTML iconHTML

    Data broadcast is an advanced technique to realize large scalability and bandwidth utilization in a mobile computing environment. In this environment, the channel bandwidth of each channel is variant with time in real cases. However, traditional schemes do not consider time-variant bandwidth of each channel to schedule data items. Therefore, the above drawback degrades the performance in generatin... View full abstract»

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  • Efficient Multidimensional Suppression for K-Anonymity

    Publication Year: 2010, Page(s):334 - 347
    Cited by:  Papers (24)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4641 KB) | HTML iconHTML

    Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. One way to enable effective data mining while preserving privacy is to anonymize the data set that includes private information about subjects before being released for data mining. One way to anonymize data set is to manipulate its content so that the records... View full abstract»

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  • Beyond Redundancies: A Metric-Invariant Method for Unsupervised Feature Selection

    Publication Year: 2010, Page(s):348 - 364
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2380 KB) | HTML iconHTML Multimedia Media

    A fundamental goal of unsupervised feature selection is denoising, which aims to identify and reduce noisy features that are not discriminative. Due to the lack of information about real classes, denoising is a challenging task. The noisy features can disturb the reasonable distance metric and result in unreasonable feature spaces, i.e., the feature spaces in which common clustering algorithms can... View full abstract»

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  • Constrained Dimensionality Reduction Using a Mixed-Norm Penalty Function with Neural Networks

    Publication Year: 2010, Page(s):365 - 380
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3891 KB) | HTML iconHTML

    Reducing the dimensionality of a classification problem produces a more computationally-efficient system. Since the dimensionality of a classification problem is equivalent to the number of neurons in the first hidden layer of a network, this work shows how to eliminate neurons on that layer and simplify the problem. In the cases where the dimensionality cannot be reduced without some degradation ... View full abstract»

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  • Ensemble Rough Hypercuboid Approach for Classifying Cancers

    Publication Year: 2010, Page(s):381 - 391
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1791 KB) | HTML iconHTML

    Cancer classification is the critical basis for patient-tailored therapy. Conventional histological analysis tends to be unreliable because different tumors may have similar appearance. The advances in microarray technology make individualized therapy possible. Various machine learning methods can be employed to classify cancer tissue samples based on microarray data. However, few methods can be e... View full abstract»

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  • k-Anonymity in the Presence of External Databases

    Publication Year: 2010, Page(s):392 - 403
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3608 KB) | HTML iconHTML

    The concept of k-anonymity has received considerable attention due to the need of several organizations to release microdata without revealing the identity of individuals. Although all previous k-anonymity techniques assume the existence of a public database (PD) that can be used to breach privacy, none utilizes PD during the anonymization process. Specifically, existing generalization algorithms ... View full abstract»

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  • PAM: An Efficient and Privacy-Aware Monitoring Framework for Continuously Moving Objects

    Publication Year: 2010, Page(s):404 - 419
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1986 KB) | HTML iconHTML

    Efficiency and privacy are two fundamental issues in moving object monitoring. This paper proposes a privacy-aware monitoring (PAM) framework that addresses both issues. The framework distinguishes itself from the existing work by being the first to holistically address the issues of location updating in terms of monitoring accuracy, efficiency, and privacy, particularly, when and how mobile clien... View full abstract»

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  • Ranked Query Processing in Uncertain Databases

    Publication Year: 2010, Page(s):420 - 436
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2937 KB) | HTML iconHTML

    Recently, many new applications, such as sensor data monitoring and mobile device tracking, raise up the issue of uncertain data management. Compared to "certain¿ data, the data in the uncertain database are not exact points, which, instead, often reside within a region. In this paper, we study the ranked queries over uncertain data. In fact, ranked queries have been studied extensively in tradit... View full abstract»

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  • Spectral Anonymization of Data

    Publication Year: 2010, Page(s):437 - 446
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1098 KB) | HTML iconHTML

    The goal of data anonymization is to allow the release of scientifically useful data in a form that protects the privacy of its subjects. This requires more than simply removing personal identifiers from the data because an attacker can still use auxiliary information to infer sensitive individual information. Additional perturbation is necessary to prevent these inferences, and the challenge is t... View full abstract»

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  • ViDE: A Vision-Based Approach for Deep Web Data Extraction

    Publication Year: 2010, Page(s):447 - 460
    Cited by:  Papers (57)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3295 KB) | HTML iconHTML

    Deep Web contents are accessed by queries submitted to Web databases and the returned data records are enwrapped in dynamically generated Web pages (they will be called deep Web pages in this paper). Extracting structured data from deep Web pages is a challenging problem due to the underlying intricate structures of such pages. Until now, a large number of techniques have been proposed to address ... View full abstract»

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  • Call for Papers: Cloud Data Management

    Publication Year: 2010, Page(s): 461
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  • New IEEE Transactions on Affective Computing

    Publication Year: 2010, Page(s): 462
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  • 7 Great Reasons for Joining the IEEE Computer Society [advertisement]

    Publication Year: 2010, Page(s): 463
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  • Raise Your Standards Software Development Certification

    Publication Year: 2010, Page(s): 464
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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