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Massive Computing Ser.: Knowledge Discovery and Data Mining : The Info-Fuzzy Network (IFN) Methodology by Mark Last and Oded Z. Maimon (2000, Hardcover)
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About this product
Product Identifiers
PublisherSpringer
ISBN-100792366476
ISBN-139780792366478
eBay Product ID (ePID)1895290
Product Key Features
Number of PagesXvii, 168 Pages
LanguageEnglish
Publication NameKnowledge Discovery and Data Mining : the Info-Fuzzy Network (Ifn) Methodology
SubjectInformation Theory, Intelligence (Ai) & Semantics, System Theory, Databases / Data Mining, Databases / General
Publication Year2000
TypeTextbook
AuthorMark Last, Oded Z. Maimon
Subject AreaComputers, Science
SeriesMassive Computing Ser.
FormatHardcover
Dimensions
Item Weight34.9 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN00-047785
Dewey Edition21
Series Volume Number1
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal006.3
Table Of ContentI 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.
SynopsisThis 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)., 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).
LC Classification NumberQA76.9.D35
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