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┌────────────────────────────────────────────────────────────────────────────┐
│■■ » RELEASE iNFORMATiON « ■■│
└─┬────────────────────────────────────────────────────────────────────────┬─┘
│░│ TiTLE : Data Mining: Know It All │░│
│░├────────────────────────────────────────────────────────────────────────┤░│
│░│ AUTHOR : Soumen Chakrabarti , Earl Cox , Eibe Frank , Ralf │░│
│░│ Hartmut G¿╣ting , Jiawei Han , Xia Jiang , Micheline │░│
│░│ Kamber , Sam S. Lightstone , Thomas P. Nadeau , Richard │░│
│░│ E Neapolitan , Dorian Pyle , Mamdouh Refaat , Markus │░│
│░│ Schneider , Toby J. Teorey , Ian H. Witten │░│
│░├────────────────────────────────────────────────────────────────────────┤░│
│░│ PUBLISHER : Morgan Kaufmann │░│
│░├────────────────────────────────────┬───────────────────────────────────┤░│
│░│ iSBN : 0123746299 │ MAKER : Team DDU │░│
│░├────────────────────────────────────┼───────────────────────────────────┤░│
│░│ PAGES : 496 Pages │ PACKAGER : Team DDU │░│
│░├────────────────────────────────────┼───────────────────────────────────┤░│
│░│ EDiTiON : November 21, 2008 │ SUPPLiER : Team DDU │░│
│░├────────────────────────────────────┼───────────────────────────────────┤░│
│░│ LANGUAGE : ENGLISH │ FORMAT : PDF │░│
│░├────────────────────────────────────┼───────────────────────────────────┤░│
│░│ RLS DATE : 04/20/2009 │ SiZE : 10.2 MB │░│
┌─┴────────────────────────────────────┴───────────────────────────────────┴─┐
│■■ » RELEASE NOTES « ■■│
└─┬────────────────────────────────────────────────────────────────────────┬─┘
│░│ │░│
│░│ [Product Information] │░│
│░│ │░│
│░│ This book brings all of the elements of data mining together in a │░│
│░│ single volume, saving the reader the time and expense of making │░│
│░│ multiple purchases. It consolidates both introductory and advanced │░│
│░│ topics, thereby covering the gamut of data mining and machine │░│
│░│ learning tactics ? from data integration and pre-processing, to │░│
│░│ fundamental algorithms, to optimization techniques and web mining │░│
│░│ methodology. The proposed book expertly combines the finest data │░│
│░│ mining material from the Morgan Kaufmann portfolio. Individual │░│
│░│ chapters are derived from a select group of MK books authored by │░│
│░│ the best and brightest in the field. These chapters are combined │░│
│░│ into one comprehensive volume in a way that allows it to be used as │░│
│░│ a reference work for those interested in new and developing aspects │░│
│░│ of data mining. This book represents a quick and efficient way to │░│
│░│ unite valuable content from leading data mining experts, thereby │░│
│░│ creating a definitive, one-stop-shopping opportunity for customers │░│
│░│ to receive the information they would otherwise need to round up │░│
│░│ from separate sources. │░│
│░│ * Chapters contributed by various recognized experts in the field │░│
│░│ let the reader remain up to date and fully informed from multiple │░│
│░│ viewpoints.* Presents multiple methods of analysis and algorithmic │░│
│░│ problem-solving techniques, enhancing the reader's technical │░│
│░│ expertise and ability to implement practical solutions. * Coverage │░│
│░│ of both theory and practice brings all of the elements of data │░│
│░│ mining together in a single volume, saving the reader the time and │░│
│░│ expense of making multiple purchases. │░│
│░│ │░│
│░│ │░│
│░│ [Install Information] │░│
│░│ │░│
│░│ Unpack it ! │░│
│░│ │░│
│░│ [For more information] │░│
│░│ │░│
│░│ http://www.amazon.com/exec/obidos/tg/detail/-/0123746299/ │░│
│░│ │░│
┌─┴────────────────────────────────────────────────────────────────────────┴─┐
│■■ » CONTACT iNFORMATiON « ■■│
└─┬────────────────────────────────────────────────────────────────────────┬─┘
│░│ │░│
│░│ Nothing but suppliers ATM! │░│
│░│ │░│
│░│ If you wish to contact us, you may email us at: │░│
│░│ Email: teamddu[at]gmail[dot]com │░│
│░│ │░│
┌─┴────────────────────────────────────────────────────────────────────────┴─┐
│■■ TEAM DDU ( Day Day UP ) 2004 x!FEAR ■■│
└────────────────────────────────────────────────────────────────────────────┘