LEADER 03762nam 2200577Ia 450 001 9910826885403321 005 20200520144314.0 010 $a9786611008062 010 $a0-08-047702-X 010 $a9781423722442 010 $a1-281-00806-0 035 $a(Au-PeEL)EBL234978 035 $a(CaPaEBR)ebr10127947 035 $a(CaONFJC)MIL100806 035 $a(OCoLC)936903533 035 $a(CaSebORM)9780120884070 035 $a(MiAaPQ)EBC234978 035 $a(PPN)191249068 035 $a(CKB)1000000000214589 035 $a(EXLCZ)991000000000214589 100 $a20050303d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData mining $epractical machine learning tools and techniques /$fIan H. Witten, Eibe Frank 205 $a2nd ed. 210 $aAmsterdam ;$aBoston, MA $cMorgan Kaufman$d2005 215 $a1 online resource (xxxi, 524 p.) $cill 225 1 $aMorgan Kaufmann series in data management systems 311 $a0-12-088407-0 311 $a1-4237-2244-2 320 $aIncludes bibliographical references (p. 485-503) and index. 327 $aPART I: MACHINE LEARNING TOOLS AND TECHNIQUES; 1 What's it all about?; 2 Input: Concepts, instances, and attributes; 3 Output: Knowledge representation; 4 Algorithms: The basic methods; 5 Credibility: Evaluating what's been learned; 6 Implementations: Real machine learning schemes; 7 Transformations: Engineering the input and output; 8 Moving on: Extensions and applications; PART II: THE WEKA MACHINE LEARNING WORKBENCH; 9 Introduction to Weka; 10 The Explorer; 11 The Knowledge Flow Interface; 12 The Experimenter; 13 The Command-Line Interface; 14 Embedded machine learning; 15 Writing New Learning Schemes; References; Index; About the Authors. 330 $aAs with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. Algorithmic methods at the heart of successful data mining including tried and true techniques as well as leading edge methods. Performance improvement techniques that work by transforming the input or output. Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization in a new, interactive interface. 410 0$aMorgan Kaufmann series in data management systems. 606 $aData mining 606 $aDatabase searching 615 0$aData mining. 615 0$aDatabase searching. 676 $a006.3 700 $aWitten$b I. H$g(Ian H.)$028571 701 $aFrank$b Eibe$028572 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826885403321 996 $aData mining$9374289 997 $aUNINA