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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).
Content Level »Research
Keywords »addition - algorithms - computer science - data mining - database - fuzzy - information - information system - knowledge - knowledge discovery - learning - machine learning - performance - process engineering - statistics
List of Figures. List of Tables. Acknowledgements. Preface. Part 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. Part II: Application Methodology and Case Studies. 5. Methodology of Application. 6. Case Studies. Part III: Comparative Study and Advanced Issues. 7. Comparative Study. 8. Advanced Data Mining Methods. 9. Summary and Some Open Problems. References. Appendices. Index.