000			02540nam a2200241Ia 4500
003			OSt
005			20250724153357.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9781439868362
040			_aCSL _beng _cCSL
041			_aeng
084			_aD65,8(B),27 Q2 TD _qCSL
100			_aHancock, Monte F. _eauthor. _9816436
245		0	_aPractical Data Mining
260			_aBoca : _bRaton C R C Press, _c2012.
300			_axxiii, 277p.
500			_aIncludes References 261-262p.; Gossary 263-268p.; Index 269-277p.
520			_aUsed by corporations, industry, and government to inform and fuel everything from focused advertising to homeland security, data mining can be a very useful tool across a wide range of applications. Unfortunately, most books on the subject are designed for the computer scientist and statistical illuminati and leave the reader largely adrift in technical waters. Revealing the lessons known to the seasoned expert, yet rarely written down for the uninitiated, Practical Data Mining explains the ins-and-outs of the detection, characterization, and exploitation of actionable patterns in data. This working field manual outlines the what, when, why, and how of data mining and offers an easy-to-follow, six-step spiral process. Catering to IT consultants, professional data analysts, and sophisticated data owners, this systematic, yet informal treatment will help readers answer questions, such as: What process model should I use to plan and execute a data mining project? How is a quantitative business case developed and assessed? What are the skills needed for different data mining projects? How do I track and evaluate data mining projects? How do I choose the best data mining techniques? Helping you avoid common mistakes, the book describes specific genres of data mining practice. Most chapters contain one or more case studies with detailed projects descriptions, methods used, challenges encountered, and results obtained. The book includes working checklists for each phase of the data mining process. Your passport to successful technical and planning discussions with management, senior scientists, and customers, these checklists lay out the right questions to ask and the right points to make from an insider's point of view. Visit the book's webpage
650			_a Data mining _9816437
650			_aComputer Science
942			_hD65,8(B),27 Q2 TD _cTEXL _2CC _n0
999			_c6372 _d6372