Data Mining: Foundations and Practice

1. Front Matter

   Pages I-XV

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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

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.

• Statistica
• algorithm
• algorithms
• classification
• clustering
• complexity
• data mining
• fuzzy
• fuzzy system
• information extraction
• kernel
• probability
• text mining
• time series analysis
• web mining

• 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

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