000			01678nam a22003137a 4500
999			_c51379 _d51379
005			20190626114331.0
008			190626b ||||| |||| 00| 0 eng d
020			_a9788126517589
082			_a005.54 SHM
100			_a Shmueli, Galit
245			_aData mining for business intelligence _b: concepts, techniques, and applications in Microsoft Office Excel with XLMiner _c/ Galit Shmueli
260			_a New Jersey _b: John Wiley & Sons, _c2007
300			_axviii, 279 pages _b: illustrations _c ; 26 cm
365			_b00
505			_a Introduction -- Overview of the data mining process -- Data exploration and dimension reduction -- Evaluating classification and predictive performance -- Multiple linear regression -- Three simple classification methods -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Association rules -- Cluster analysis -- Cases.
520			_aLearn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success
650			_aComputer science, information & general works
650			_a Computer programming, programs & data
650			_aMicrosoft Excel (Computer file)
650			_aData mining.
650			_aDataprocessing.
650			_aStatistiek.
650			_aBusiness -- Data processing.
700			_aShmueli, Galit,
700			_a Bruce, Peter C.
700			_aPatel, Nitin R
700			_aTorgo, Luís
942			_cBOOK