Knowledge Discovery and Data Mining

1. Front Matter

   Pages i-xvii

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2. Information-Theoretic Approach to Knowledge Discovery

   1. Front Matter

      Pages 1-1

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   2. Introduction

      • Oded Maimon, Mark Last

      Pages 3-21

   3. Automated Data Pre-Processing

      • Oded Maimon, Mark Last

      Pages 23-29

   4. Information-Theoretic Connectionist Networks

      • Oded Maimon, Mark Last

      Pages 31-51

   5. Post-Processing of Data Mining Results

      • Oded Maimon, Mark Last

      Pages 53-59

3. Application Methodology and Case Studies

   1. Front Matter

      Pages 61-61

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   2. Methodology of Application

      • Oded Maimon, Mark Last

      Pages 63-70

   3. Case Studies

      • Oded Maimon, Mark Last

      Pages 71-103

4. Comparative Study and Advanced Issues

   1. Front Matter

      Pages 105-105

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   2. Comparative Study

      • Oded Maimon, Mark Last

      Pages 107-121

   3. Advanced data mining methods

      • Oded Maimon, Mark Last

      Pages 123-133

   4. Summary and Some Open Problems

      • Oded Maimon, Mark Last

      Pages 135-140

5. Back Matter

   Pages 141-168

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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).

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

• Tel-Aviv University, Tel-Aviv, Israel

  Oded Maimon

• University of South Florida, Tampa, USA

  Mark Last

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