Maimon O., Last M. Knowledge Discovery and Data Mining. The Info-Fuzzy Network (IFN) Methodology

Springer, 2001. — 170 p.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 modeling and generalizing common patterns from large sets of multi-type data. DM is part of KDD, which is the overall process for Knowledge Discovery Databases. The accessibility and abundance of information today makes this a topic of particular importance and need.. 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 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) 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). Finally, we refer the readers to the book's web site, where a copy of IFN program and data can be downloaded and experimented with. This is a "live" web site, meaning that we will update the program periodically and add more examples and case studies.Part I Information-Theoretic Approach to Knowledge DiscoveryAutomated Data Pre-Processing
Information-Theoretic Connectionist Networks
Post-Processing of Data Mining Results
Part II Application Methodology and Case Studies
Methodology of Application
Case Studies
Part III Comparative Study and Advanced Issues
Comparative Study
Advanced Data Mining Methods
Summary and Some Open Problems
A: Information Theory – An Overview
B: Detailed Results