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2008 Eighth IEEE International Conference on Data Mining

Date 15-19 Dec. 2008

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Displaying Results 1 - 25 of 162
  • [Front cover]

    Publication Year: 2008, Page(s): C1
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  • [Title page i]

    Publication Year: 2008, Page(s): i
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  • [Title page iii]

    Publication Year: 2008, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2008, Page(s): iv
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  • Table of contents

    Publication Year: 2008, Page(s):v - xiii
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  • Welcome Message from the Conference Chairs

    Publication Year: 2008, Page(s): xiv
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  • Welcome to ICDM 2008

    Publication Year: 2008, Page(s):xv - xvi
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  • Conference organization

    Publication Year: 2008, Page(s):xvii - xviii
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  • Steering Committee

    Publication Year: 2008, Page(s): xix
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  • Program Committee

    Publication Year: 2008, Page(s):xx - xxv
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  • Non-PC reviewers

    Publication Year: 2008, Page(s):xxvi - xxix
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  • Corporate sponsors

    Publication Year: 2008, Page(s): xxx
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  • Invited Talks

    Publication Year: 2008, Page(s): xxxi
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (93 KB) | HTML iconHTML

    Provides an abstract for each of the invited presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Tutorials

    Publication Year: 2008, Page(s):xxxii - xxxiii
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  • Workshops

    Publication Year: 2008, Page(s):xxxiv - xxxvi
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  • Panel session ICDM 2008: Introduction to ICDM'08 Panel Session Social Networks and Data Mining: Where's the Beef?

    Publication Year: 2008, Page(s): xxxvii
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (62 KB)

    Provides an abstract of the panel presentation and a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings. View full abstract»

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  • On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking

    Publication Year: 2008, Page(s):3 - 12
    Cited by:  Papers (42)  |  Patents (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (289 KB) | HTML iconHTML

    This paper presents online topic model (OLDA), a topic model that automatically captures the thematic patterns and identifies emerging topics of text streams and their changes over time. Our approach allows the topic modeling framework, specifically the latent Dirichlet allocation (LDA) model, to work in an online fashion such that it incrementally builds an up-to-date model (mixture of topics per... View full abstract»

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  • Unsupervised Cross-Domain Learning by Interaction Information Co-clustering

    Publication Year: 2008, Page(s):13 - 22
    Cited by:  Papers (1)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (739 KB) | HTML iconHTML

    In real-world data mining applications, one often has access to multiple datasets that are relevant to the task at hand. However, learning from such datasets can be difficult as they are often drawn from different domains, i.e., not identically distributed or differ in class or feature sets. In this paper, we consider the problem of learning the class structures %, unique and shared, of related do... View full abstract»

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  • Paired Learners for Concept Drift

    Publication Year: 2008, Page(s):23 - 32
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (500 KB) | HTML iconHTML

    To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas are active learner predicts based on its experience over a short, recent window of time. The method of paired learning uses differences in accuracy between the two learners over this window to determine when to replace the current stable learner, sinc... View full abstract»

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  • Predicting Future Decision Trees from Evolving Data

    Publication Year: 2008, Page(s):33 - 42
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (328 KB) | HTML iconHTML

    Recognizing and analyzing change is an important human virtue because it enables us to anticipate future scenarios and thus allows us to act pro-actively. One approach to understand change within a domain is to analyze how models and patterns evolve. Knowing how a model changes over time is suggesting to ask: Can we use this knowledge to learn a model in anticipation, such that it better reflects ... View full abstract»

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  • A Randomized Approach for Approximating the Number of Frequent Sets

    Publication Year: 2008, Page(s):43 - 52
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (334 KB) | HTML iconHTML

    We investigate the problem of counting the number of frequent (item)sets - a problem known to be intractable in terms of an exact polynomial time computation. In this paper, we show that it is in general also hard to approximate. Subsequently, a randomized counting algorithm is developed using the Markov chain Monte Carlo method. While for general inputs an exponential running time is needed in or... View full abstract»

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  • A Non-parametric Semi-supervised Discretization Method

    Publication Year: 2008, Page(s):53 - 62
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (313 KB) | HTML iconHTML

    Semi-supervised classification methods aim to exploit labelled and unlabelled examples to train a predictive model. Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method which adopts very low informative prior on data. This method discretizes the numerical domain of a continuous input variable, while keepin... View full abstract»

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  • Non-negative Matrix Factorization on Manifold

    Publication Year: 2008, Page(s):63 - 72
    Cited by:  Papers (61)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (261 KB) | HTML iconHTML

    Recently non-negative matrix factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed version of the original data matrix. The solu... View full abstract»

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  • Anti-monotonic Overlap-Graph Support Measures

    Publication Year: 2008, Page(s):73 - 82
    Cited by:  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (234 KB) | HTML iconHTML

    In graph mining, a frequency measure is anti-monotonic if the frequency of a pattern never exceeds the frequency of a subpattern. The efficiency and correctness of most graph pattern miners relies critically on this property. We study the case where the dataset is a single graph. Vanetik, Gudes and Shimony already gave sufficient and necessary conditions for anti-monotonicity of measures depending... View full abstract»

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  • SeqStream: Mining Closed Sequential Patterns over Stream Sliding Windows

    Publication Year: 2008, Page(s):83 - 92
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (339 KB) | HTML iconHTML

    Previous studies have shown mining closed patterns provides more benefits than mining the complete set of frequent patterns, since closed pattern mining leads to more compact results and more efficient algorithms. It is quite useful in a data stream environment where memory and computation power are major concerns. This paper studies the problem of mining closed sequential patterns over data strea... View full abstract»

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