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Data Mining: Foundations and Practice

  • Book
  • © 2008

Overview

Editors:
  1. Tsau Young Lin

    1. Department of Computer Science, San Jose State University, San Jose, USA

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  2. Ying Xie

    1. Department of Computer Science and Information Systems, Kennesaw State University, Kennesaw, USA

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  3. Anita Wasilewska

    1. Department of Computer Science, The University at Stony Brook, Stony Brook, USA

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  4. Churn-Jung Liau

    1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

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  • Presents foundations and practice of Data Mining
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 118)

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Table of contents (32 chapters)

  1. Front Matter

    Pages I-XV
    Download chapter PDF

  2. Compact Representations of Sequential Classification Rules

    • Elena Baralis, Silvia Chiusano, Riccardo Dutto, Luigi Mantellini
    Pages 1-30

  3. An Algorithm for Mining Weighted Dense Maximal 1-Complete Regions

    • Haiyun Bian, Raj Bhatnagar
    Pages 31-48

  4. Mining Linguistic Trends from Time Series

    • Chun-Hao Chen, Tzung-Pei Hong, Vincent S. Tseng
    Pages 49-60

  5. Latent Semantic Space for Web Clustering

    • I. Jen Chiang, Tsau Young (‘T. Y.’) Lin, Hsiang-Chun Tsai, Jau-Min Wong, Xiaohua Hu
    Pages 61-77

  6. A Logical Framework for Template Creation and Information Extraction

    • David Corney, Emma Byrne, Bernard Buxton, David Jones
    Pages 79-108

  7. A Bipolar Interpretation of Fuzzy Decision Trees

    • Tuan-Fang Fan, Churn-Jung Liau, Duen-Ren Liu
    Pages 109-123

  8. A Probability Theory Perspective on the Zadeh Fuzzy System

    • Qing Shi Gao, Xiao Yu Gao, Lei Xu
    Pages 125-137

  9. Three Approaches to Missing Attribute Values: A Rough Set Perspective

    • Jerzy W. Grzymala-Busse
    Pages 139-152

  10. MLEM2 Rule Induction Algorithms: With and Without Merging Intervals

    • Jerzy W. Grzymala-Busse
    Pages 153-164

  11. Towards a Methodology for Data Mining Project Development: The Importance of Abstraction

    • P. González-Aranda, E. Menasalvas, S. Millán, Carlos Ruiz, J. Segovia
    Pages 165-178

  12. Fining Active Membership Functions in Fuzzy Data Mining

    • Tzung-Pei Hong, Chun-Hao Chen, Yu-Lung Wu, Vincent S. Tseng
    Pages 179-196

  13. A Compressed Vertical Binary Algorithm for Mining Frequent Patterns

    • J. Hdez. Palancar, R. Hdez. León, J. Medina Pagola, A. Hechavarría
    Pages 197-211

  14. Naïve Rules Do Not Consider Underlying Causality

    • Lawrence J. Mazlack
    Pages 213-229

  15. Inexact Multiple-Grained Causal Complexes

    • Lawrence J. Mazlack
    Pages 231-249

  16. Does Relevance Matter to Data Mining Research?

    • Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal
    Pages 251-275

  17. E-Action Rules

    • Li-Shiang Tsay, Zbigniew W. Raś
    Pages 277-288

  18. Mining E-Action Rules, System DEAR

    • Zbigniew W. Raś, Li-Shiang Tsay
    Pages 289-298

  19. Definability of Association Rules and Tables of Critical Frequencies

    • Jan Rauch
    Pages 299-313

  20. Classes of Association Rules: An Overview

    • Jan Rauch
    Pages 315-337
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Keywords

About this book

The IEEE ICDM 2004 workshop on the Foundation of Data Mining and the IEEE ICDM 2005 workshop on the Foundation of Semantic Oriented Data and Web Mining focused on topics ranging from the foundations of data mining to new data mining paradigms. The workshops brought together both data mining researchers and practitioners to discuss these two topics while seeking solutions to long standing data mining problems and stimul- ing new data mining research directions. We feel that the papers presented at these workshops may encourage the study of data mining as a scienti?c ?eld and spark new communications and collaborations between researchers and practitioners. Toexpressthevisionsforgedintheworkshopstoawiderangeofdatam- ing researchers and practitioners and foster active participation in the study of foundations of data mining, we edited this volume by involving extended and updated versions of selected papers presented at those workshops as well as some other relevant contributions. The content of this book includes st- ies of foundations of data mining from theoretical, practical, algorithmical, and managerial perspectives. The following is a brief summary of the papers contained in this book.

Editors and Affiliations

  • Department of Computer Science, San Jose State University, San Jose, USA

    Tsau Young Lin

  • Department of Computer Science and Information Systems, Kennesaw State University, Kennesaw, USA

    Ying Xie

  • Department of Computer Science, The University at Stony Brook, Stony Brook, USA

    Anita Wasilewska

  • Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Churn-Jung Liau

Bibliographic Information

  • Book Title: Data Mining: Foundations and Practice

  • Editors: Tsau Young Lin, Ying Xie, Anita Wasilewska, Churn-Jung Liau

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-540-78488-3

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2008

  • Hardcover ISBN: 978-3-540-78487-6Published: 20 August 2008

  • Softcover ISBN: 978-3-642-09722-5Published: 23 December 2010

  • eBook ISBN: 978-3-540-78488-3Published: 17 August 2008

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XVI, 562

  • Number of Illustrations: 104 b/w illustrations, 25 illustrations in colour

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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