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Foundations of Data Mining and Knowledge Discovery

  • Book
  • © 2005

Overview

Editors:
  1. Tsau Young Lin
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  2. Setsuo Ohsuga
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  3. Churn-Jung Liau
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  4. Xiaohua Hu
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  5. Shusaku Tsumoto
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  • Collection of expanded versions of selected papers originally presented at the IEEE ICDM 2002 workshop on the Foundation of Data Mining and Discovery
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 6)

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Table of contents (21 chapters)

  1. Front Matter

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  2. Knowledge Discovery as Translation

    • Setsuo Ohsuga
    Pages 1-19

  3. Mathematical Foundation of Association Rules – Mining Associations by Solving Integral Linear Inequalities

    • T.Y. Lin
    Pages 21-42

  4. Comparative Study of Sequential Pattern Mining Models

    • Hye-Chung (Monica) Kum, Susan Paulsen, Wei Wang
    Pages 43-70

  5. Designing Robust Regression Models

    • Murlikrishna Viswanathan, Kotagiri Ramamohanarao
    Pages 71-86

  6. A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases

    • Ying Xie, Vijay V. Raghavan
    Pages 87-100

  7. A Careful Look at the Use of Statistical Methodology in Data Mining

    • Norman Matloff
    Pages 101-117

  8. Justification and Hypothesis Selection in Data Mining

    • Tuan-Fang Fan, Duen-Ren Liu, Churn-Jung Liau
    Pages 119-130

  9. On Statistical Independence in a Contingency Table

    • Shusaku Tsumoto
    Pages 131-141

  10. A Comparative Investigation on Model Selection in Binary Factor Analysis

    • Yujia An, Xuelei Hu, Lei Xu
    Pages 143-160

  11. Extraction of Generalized Rules with Automated Attribute Abstraction

    • Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura
    Pages 161-170

  12. Decision Making Based on Hybrid of Multi-Knowledge and Naïve Bayes Classifier

    • QingXiang Wu, David Bell, Martin McGinnity, Gongde Guo
    Pages 171-184

  13. First-Order Logic Based Formalism for Temporal Data Mining*

    • Paul Cotofrei, Kilian Stoffel
    Pages 185-210

  14. An Alternative Approach to Mining Association Rules

    • Jan Rauch, Milan Šimůnek
    Pages 211-231

  15. Direct Mining of Rules from Data with Missing Values

    • Vladimir Gorodetsky, Oleg Karsaev, Vladimir Samoilov
    Pages 233-264

  16. Cluster Identification Using Maximum Configuration Entropy

    • C.H. Li
    Pages 265-276

  17. Mining Small Objects in Large Images Using Neural Networks

    • Mengjie Zhang
    Pages 277-303

  18. Improved Knowledge Mining with the Multimethod Approach

    • Mitja Lenič, Peter Kokol, Milan Zorman, Petra Povalej, Bruno Stiglic, Ryuichi Yamamoto
    Pages 305-318

  19. Posting Act Tagging Using Transformation-Based Learning

    • Tianhao Wu, Faisal M. Khan, Todd A. Fisher, Lori A. Shuler, William M. Pottenger
    Pages 319-331

  20. Identification of Critical Values in Latent Semantic Indexing

    • April Kontostathis, William M. Pottenger, Brian D. Davison
    Pages 333-346
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Keywords

About this book

"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.

Bibliographic Information

  • Book Title: Foundations of Data Mining and Knowledge Discovery

  • Editors: Tsau Young Lin, Setsuo Ohsuga, Churn-Jung Liau, Xiaohua Hu, Shusaku Tsumoto

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/b137220

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2005

  • Hardcover ISBN: 978-3-540-26257-2Published: 02 September 2005

  • Softcover ISBN: 978-3-642-43228-6Published: 16 November 2014

  • eBook ISBN: 978-3-540-32408-9Published: 22 August 2005

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XIII, 378

  • Topics: Theory of Computation, Mathematical and Computational Engineering, Artificial Intelligence

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