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IEEE Transactions on Knowledge and Data Engineering

Issue 12 • Date Dec. 2009

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Displaying Results 1 - 18 of 18
  • [Front cover]

    Publication Year: 2009, Page(s): c1
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  • [Inside front cover]

    Publication Year: 2009, Page(s): c2
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  • A Model-Based Approach for Discrete Data Clustering and Feature Weighting Using MAP and Stochastic Complexity

    Publication Year: 2009, Page(s):1649 - 1664
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3838 KB) | HTML iconHTML

    In this paper, we consider the problem of unsupervised discrete feature selection/weighting. Indeed, discrete data are an important component in many data mining, machine learning, image processing, and computer vision applications. However, much of the published work on unsupervised feature selection has concentrated on continuous data. We propose a probabilistic approach that assigns relevance w... View full abstract»

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  • Clustering with Local and Global Regularization

    Publication Year: 2009, Page(s):1665 - 1678
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4702 KB) | HTML iconHTML

    Clustering is an old research topic in data mining and machine learning. Most of the traditional clustering methods can be categorized as local or global ones. In this paper, a novel clustering method that can explore both the local and global information in the data set is proposed. The method, Clustering with Local and Global Regularization (CLGR), aims to minimize a cost function that properly ... View full abstract»

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  • Continuous K-Means Monitoring with Low Reporting Cost in Sensor Networks

    Publication Year: 2009, Page(s):1679 - 1691
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1259 KB) | HTML iconHTML

    In this paper, we study an interesting problem: continuously monitoring k-means clustering of sensor readings in a large sensor network. Given a set of sensors whose readings evolve over time, we want to maintain the k-means of the readings continuously. The optimization goal is to reduce the reporting cost in the network, that is, let as few sensors as possible report their current readings to th... View full abstract»

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  • Discovering Transitional Patterns and Their Significant Milestones in Transaction Databases

    Publication Year: 2009, Page(s):1692 - 1707
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4493 KB) | HTML iconHTML

    A transaction database usually consists of a set of time-stamped transactions. Mining frequent patterns in transaction databases has been studied extensively in data mining research. However, most of the existing frequent pattern mining algorithms (such as Apriori and FP-growth) do not consider the time stamps associated with the transactions. In this paper, we extend the existing frequent pattern... View full abstract»

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  • Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases

    Publication Year: 2009, Page(s):1708 - 1721
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3288 KB) | HTML iconHTML

    Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calc... View full abstract»

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  • Join of Multiple Data Streams in Sensor Networks

    Publication Year: 2009, Page(s):1722 - 1736
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1852 KB) | HTML iconHTML

    Sensor networks are multihop wireless networks of resource-constrained sensor nodes used to realize high-level collaborative sensing tasks. To query or access data generated by the sensor nodes, the sensor network can be viewed as a distributed database. In this paper, we develop algorithms for communication-efficient implementation of join of multiple (two or more) data streams in a sensor networ... View full abstract»

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  • Locating XML Documents in a Peer-to-Peer Network Using Distributed Hash Tables

    Publication Year: 2009, Page(s):1737 - 1752
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2000 KB) | HTML iconHTML

    One of the key challenges in a peer-to-peer (P2P) network is to efficiently locate relevant data sources across a large number of participating peers. With the increasing popularity of the extensible markup language (XML) as a standard for information interchange on the Internet, XML is commonly used as an underlying data model for P2P applications to deal with the heterogeneity of data and enhanc... View full abstract»

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  • Pooling for Combination of Multilevel Forecasts

    Publication Year: 2009, Page(s):1753 - 1766
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1990 KB) | HTML iconHTML

    In this paper, we provide a theoretical analysis of effects of applying different forecast diversification methods on the structure of the forecast error covariance matrices and decomposed forecast error components based on the bias-variance-Bayes error decomposition of James and Hastie. We express the "diversityrdquo of different forecasts in relation to different error components and propose a m... View full abstract»

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  • Privacy-Preserving Tuple Matching in Distributed Databases

    Publication Year: 2009, Page(s):1767 - 1782
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1366 KB) | HTML iconHTML

    We address the problems of privacy-preserving duplicate tuple matching (PPDTM) and privacy-preserving threshold attributes matching (PPTAM) in the scenario of a horizontally partitioned database among N parties, where each party holds a private share of the database's tuples and all tuples have the same set of attributes. In PPDTM, each party determines whether its tuples have any duplicate on oth... View full abstract»

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  • Tuning On-Air Signatures for Balancing Performance and Confidentiality

    Publication Year: 2009, Page(s):1783 - 1797
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2881 KB) | HTML iconHTML

    In this paper, we investigate the trade off between performance and confidentiality in signature-based air indexing schemes for wireless data broadcast. Two metrics, namely, false drop probability and false guess probability, are defined to quantify the filtering efficiency and confidentiality loss of a signature scheme. Our analysis reveals that false drop probability and false guess probability ... View full abstract»

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  • Local Kernel Regression Score for Selecting Features of High-Dimensional Data

    Publication Year: 2009, Page(s):1798 - 1802
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1193 KB) | HTML iconHTML

    In general, irrelevant features of high-dimensional data will degrade the performance of an inference system, e.g., a clustering algorithm or a classifier. In this paper, we therefore present a Local Kernel Regression (LKR) scoring approach to evaluate the relevancy of features based on their capabilities of keeping the local configuration in a small patch of data. Accordingly, a score index featu... View full abstract»

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  • Manufacturing-Oriented Discrete Process Modeling Approach Using the Predicate Logic

    Publication Year: 2009, Page(s):1803 - 1806
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (861 KB) | HTML iconHTML

    Part machining is a discrete manufacturing process. In order to evaluate the manufacturing process, an intelligent modeling method based on the first-order predicate logic is proposed. First, the basic predicate formula is defined according to the machining method, and the predicate and variables are illustrated in detail. Thus, the process representation is completed. Second, to construct the pro... View full abstract»

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  • IEEE Computer Society 2010 New Student Member Package

    Publication Year: 2009, Page(s): 1807
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  • IEEE Computer Society Computing Now [advertisement]

    Publication Year: 2009, Page(s): 1808
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  • TKDE Information for authors

    Publication Year: 2009, Page(s): c3
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  • [Back cover]

    Publication Year: 2009, 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