LEADER 05156nam 2200673Ia 450 001 9910451300103321 005 20200520144314.0 010 $a1-281-37904-2 010 $a9786611379049 010 $a981-277-363-0 035 $a(CKB)1000000000407078 035 $a(EBL)1681620 035 $a(OCoLC)879025456 035 $a(SSID)ssj0000190828 035 $a(PQKBManifestationID)11183579 035 $a(PQKBTitleCode)TC0000190828 035 $a(PQKBWorkID)10180261 035 $a(PQKB)11131745 035 $a(MiAaPQ)EBC1681620 035 $a(WSP)00006103 035 $a(PPN)180686437 035 $a(Au-PeEL)EBL1681620 035 $a(CaPaEBR)ebr10201384 035 $a(CaONFJC)MIL137904 035 $a(EXLCZ)991000000000407078 100 $a20061120d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aLecture notes in data mining$b[electronic resource] /$fedited by Michael W. Berry, Murray Browne 210 $aHackensack, NJ $cWorld Scientific$dc2006 215 $a1 online resource (238 p.) 300 $aDescription based upon print version of record. 311 $a981-256-802-6 320 $aIncludes bibliographical references and index. 327 $aCONTENTS ; 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 Classification 327 $a4. 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 Algorithms 327 $a4. 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. Summary 327 $a6 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. C5.0 327 $a6. 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. Motivation 327 $a3. The Clustering Process 330 $a 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 expla 606 $aData mining 606 $aDatabase searching 608 $aElectronic books. 615 0$aData mining. 615 0$aDatabase searching. 676 $a005.741 701 $aBerry$b Michael W$092312 701 $aBrowne$b Murray$0726265 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910451300103321 996 $aLecture notes in data mining$92110886 997 $aUNINA