Data Mining: Foundations and Practice

  • Tsau Young Lin
  • Ying Xie
  • Anita Wasilewska
  • Churn-Jung Liau
Part of the Studies in Computational Intelligence book series (SCI, volume 118)

Table of contents

  1. Front Matter
    Pages I-XV
  2. Elena Baralis, Silvia Chiusano, Riccardo Dutto, Luigi Mantellini
    Pages 1-30
  3. Chun-Hao Chen, Tzung-Pei Hong, Vincent S. Tseng
    Pages 49-60
  4. I. Jen Chiang, Tsau Young (‘T. Y.’) Lin, Hsiang-Chun Tsai, Jau-Min Wong, Xiaohua Hu
    Pages 61-77
  5. David Corney, Emma Byrne, Bernard Buxton, David Jones
    Pages 79-108
  6. Tuan-Fang Fan, Churn-Jung Liau, Duen-Ren Liu
    Pages 109-123
  7. Qing Shi Gao, Xiao Yu Gao, Lei Xu
    Pages 125-137
  8. P. González-Aranda, E. Menasalvas, S. Millán, Carlos Ruiz, J. Segovia
    Pages 165-178
  9. Tzung-Pei Hong, Chun-Hao Chen, Yu-Lung Wu, Vincent S. Tseng
    Pages 179-196
  10. J. Hdez. Palancar, R. Hdez. León, J. Medina Pagola, A. Hechavarría
    Pages 197-211
  11. Lawrence J. Mazlack
    Pages 213-229
  12. Lawrence J. Mazlack
    Pages 231-249
  13. Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal
    Pages 251-275
  14. Li-Shiang Tsay, Zbigniew W. Raś
    Pages 277-288
  15. Zbigniew W. Raś, Li-Shiang Tsay
    Pages 289-298

About this book

Introduction

This book contains valuable studies in data mining from both foundational and practical perspectives. The foundational studies of data mining may help to lay a solid foundation for data mining as a scientific discipline, while the practical studies of data mining may lead to new data mining paradigms and algorithms.

The foundational studies contained in this book focus on a broad range of subjects, including conceptual framework of data mining, data preprocessing and data mining as generalization, probability theory perspective on fuzzy systems, rough set methodology on missing values, inexact multiple-grained causal complexes, complexity of the privacy problem, logical framework for template creation and information extraction, classes of association rules, pseudo statistical independence in a contingency table, and role of sample size and determinants in granularity of contingency matrix.

The practical studies contained in this book cover different fields of data mining, including rule mining, classification, clustering, text mining, Web mining, data stream mining, time series analysis, privacy preservation mining, fuzzy data mining, ensemble approaches, and kernel based approaches.

We believe that the works presented in this book will encourage the study of data mining as a scientific field and spark collaboration among researchers and practitioners.

Keywords

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

Editors and affiliations

  • Tsau Young Lin
    • 1
  • Ying Xie
    • 2
  • Anita Wasilewska
    • 3
  • Churn-Jung Liau
    • 4
  1. 1.Department of Computer ScienceSan Jose State UniversitySan JoseUSA
  2. 2.Department of Computer Science and Information SystemsKennesaw State UniversityKennesawUSA
  3. 3.Department of Computer ScienceThe University at Stony BrookStony BrookUSA
  4. 4.Institute of Information ScienceAcademia SinicaTaipeiTaiwan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-78488-3
  • Copyright Information Springer Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-78487-6
  • Online ISBN 978-3-540-78488-3
  • Series Print ISSN 1860-949X