05122nam 2200661Ia 450 991082039160332120200520144314.01-281-37904-29786611379049981-277-363-0(CKB)1000000000407078(EBL)1681620(OCoLC)879025456(SSID)ssj0000190828(PQKBManifestationID)11183579(PQKBTitleCode)TC0000190828(PQKBWorkID)10180261(PQKB)11131745(MiAaPQ)EBC1681620(WSP)00006103(Au-PeEL)EBL1681620(CaPaEBR)ebr10201384(CaONFJC)MIL137904(PPN)180686437(EXLCZ)99100000000040707820061120d2006 uy 0engur|n|---|||||txtccrLecture notes in data mining[electronic resource] /edited by Michael W. Berry, Murray BrowneHackensack, NJ World Scientificc20061 online resource (238 p.)Description based upon print version of record.981-256-802-6 Includes bibliographical references and index.CONTENTS ; Preface ; 1 Point Estimation Algorithms ; 1. Introduction ; 2. Motivation ; 3. Methods of Point Estimation ; 4. Measures of Performance ; 5. Summary ; 2 Applications of Bayes Theorem ; 1. Introduction ; 2. Motivation ; 3. The Bayes Approach for Classification4. Examples 5. Summary ; 3 Similarity Measures ; 1. Introduction ; 2. Motivation ; 3. Classic Similarity Measures ; 4. Example ; 5. Current Applications ; 6. Summary ; 4 Decision Trees ; 1. Introduction ; 2. Motivation ; 3. Decision Tree Algorithms4. Example: Classification of University Students 5. Applications of Decision Tree Algorithms ; 6. Summary ; 5 Genetic Algorithms ; 1. Introduction ; 2. Motivation ; 3. Fundamentals ; 4. Example: The Traveling-Salesman ; 5. Current and Future Applications ; 6. Summary6 Classification: Distance-based Algorithms 1. Introduction ; 2. Motivation ; 3. Distance Functions ; 4. Classification Algorithms ; 5. Current Applications ; 6. Summary ; 7 Decision Tree-based Algorithms ; 1. Introduction ; 2. Motivation ; 3. ID3 ; 4. C4.5 ; 5. C5.06. CART 7. Summary ; 8 Covering (Rule-based) Algorithms ; 1. Introduction ; 2. Motivation ; 3. Classification Rules ; 4. Covering (Rule-based) Algorithms ; 5. Applications of Covering Algorithms ; 6. Summary ; 9 Clustering: An Overview ; 1. Introduction ; 2. Motivation3. The Clustering Process The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaData miningDatabase searchingData mining.Database searching.005.741Berry Michael W92312Browne Murray726265MiAaPQMiAaPQMiAaPQBOOK9910820391603321Lecture notes in data mining3926460UNINA