Knowledge Discovery and Data Mining

The Info-Fuzzy Network (IFN) Methodology

  • Oded Maimon
  • Mark Last

Part of the Massive Computing book series (MACO, volume 1)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Information-Theoretic Approach to Knowledge Discovery

    1. Front Matter
      Pages 1-1
    2. Oded Maimon, Mark Last
      Pages 3-21
    3. Oded Maimon, Mark Last
      Pages 23-29
    4. Oded Maimon, Mark Last
      Pages 31-51
    5. Oded Maimon, Mark Last
      Pages 53-59
  3. Application Methodology and Case Studies

    1. Front Matter
      Pages 61-61
    2. Oded Maimon, Mark Last
      Pages 63-70
    3. Oded Maimon, Mark Last
      Pages 71-103
  4. Comparative Study and Advanced Issues

    1. Front Matter
      Pages 105-105
    2. Oded Maimon, Mark Last
      Pages 107-121
    3. Oded Maimon, Mark Last
      Pages 123-133
    4. Oded Maimon, Mark Last
      Pages 135-140
  5. Back Matter
    Pages 141-168

About this book

Introduction

This book presents a specific and unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network methodology. Data Mining (DM) is the science of modelling and generalizing common patterns from large sets of multi-type data. DM is a part of KDD, which is the overall process for Knowledge Discovery in Databases. The accessibility and abundance of information today makes this a topic of particular importance and need. The book has three main parts complemented by appendices as well as software and project data that are accessible from the book's web site (http://www.eng.tau.ac.iV-maimonlifn-kdg£). Part I (Chapters 1-4) starts with the topic of KDD and DM in general and makes reference to other works in the field, especially those related to the information theoretic approach. The remainder of the book presents our work, starting with the IFN theory and algorithms. Part II (Chapters 5-6) discusses the methodology of application and includes case studies. Then in Part III (Chapters 7-9) a comparative study is presented, concluding with some advanced methods and open problems. The IFN, being a generic methodology, applies to a variety of fields, such as manufacturing, finance, health care, medicine, insurance, and human resources. The appendices expand on the relevant theoretical background and present descriptions of sample projects (including detailed results).

Keywords

addition algorithms computer science data mining database fuzzy information information system knowledge knowledge discovery learning machine learning performance process engineering statistics

Authors and affiliations

  • Oded Maimon
    • 1
  • Mark Last
    • 2
  1. 1.Tel-Aviv UniversityTel-AvivIsrael
  2. 2.University of South FloridaTampaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-3296-2
  • Copyright Information Springer-Verlag US 2001
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-4842-7
  • Online ISBN 978-1-4757-3296-2
  • Series Print ISSN 1569-2698
  • Series Online ISSN 2468-8738
  • About this book