000			02090nam a2200349Ia 4500
005			20260803094046.0
008			251212s2007 xx 000 0 und d
245	1		_aIntroduction to business data mining / _cDavid Olson.
100			_aOlson, David
700			_aShi, Yong
260			_aNew York : _bMcGraw-Hill, _c2007.
300			_axiii, 273 p.:
300			_c26 cm.
500			_aIncludes index
020			_a007-124470-0
650			_aBusiness _xData processing
650			_aData mining
050			_a006.312 Ol84i
943			_b0 _c0 _d0 _e1 _f0 _g0 _h0 _i0 _j0
944			_b1 _c1 _d0
953			_pHED2579
546			_aEnglish
953			_cCollLib
505			_aPart I: INTRODUCTION 1: Initial Description of Data Mining in Business 2: Data Mining Processes and Knowledge Discovery 3: Database Support to Data Mining Part II: DATA MINING METHODS AS TOOLS 4: Overview of Data Mining Techniques 4 Appendix: Enterprise Miner Demonstration on Expenditure Data Set 5: Cluster Analysis 5 Appendix: Clementine 6: Regression Algorithms in Data Mining 7: Neural Networks in Data Mining 8: Decision Tree Algorithms 8 Appendix: Demonstration of See5 Decision Tree Analysis 9: Linear Programming-Based Methods 9 Appendix: Data Mining Linear Programming Formulations Part III: BUSINESS APPLICATIONS 10: Business Data Mining Applications Applications 11: Market-Basket Analysis 11 Appendix: Market-Basket Procedure Part IV: DEVELOPING ISSUES 12: Text and Web Mining 12 Appendix: Semantic Text Analysis 13: Ethical Aspects of Data Mining
520			_aIntroduces students to the fundamental concepts of data mining. This text shows readers how to gather and analyze large sets of data to gain useful business understanding. It also describes and demonstrated basic data mining algorithms, and presents an overview of the developing areas in this field, including web mining, text mining, and more.
591			_a0
336			_aText
337			_aUnmediated
338			_aVolume
942			_2lcc _cBK _arizal _b2015-04-07 23:18:18 _drizal _e2015-04-07 23:18:18
998			_a3422
999			_c3409 _d3409