Overview

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).

Full Product Details

Author:   O. Maimon ,  M. Last
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of hardcover 1st ed. 2001
Volume:   1
Dimensions:   Width: 15.50cm , Height: 1.00cm , Length: 23.50cm
Weight:   0.454kg
ISBN:  

9781441948427

ISBN 10:   1441948422
Pages:   168
Publication Date:   07 December 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

I Information-Theoretic Approach to Knowledge Discovery.- 1 Introduction.- 2 Automated Data Pre-Processing.- 3 Information-Theoretic Connectionist Networks.- 4 Post-Processing of Data Mining Results.- II Application Methodology and Case Studies.- 5 Methodology of Application.- 6 Case Studies.- III Comparative Study and Advanced Issues.- 7 Comparative Study.- 8 Advanced data mining methods.- 9 Summary and Some Open Problems.- Appendix A.- 1. Entropy H(X).- 2. Joint Entropy and Conditional Entropy.- 3. Relative Entropy and Mutual Information.- Appendix B.- 1. Breast Cancer Database.- 2. Chess Endgames.- 3. Credit Approval Database.- 4. Diabetes Database.- 5. Glass Identification Database.- 6. Heart Disease (Cleveland) Database.- 7. Iris Plants Database.- 8. Liver Database.- 9. Lung Cancer Database.- 10. Wine Database.

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