000			04405cam a2200457 a 4500
999			_c8150 _d8150
001			17220962
005			20210216113843.0
008			120321s2012 flua b 001 0 eng d
010			_a 2012008925
016	7		_a016039623 _2Uk
020			_a9781466503960 (alk. paper)
020			_a1466503963 (alk. paper)
035			_a(OCoLC)ocn756596227
040			_aDLC _beng _cDLC _dYDX _dBTCTA _dUKMGB _dYDXCP _dOCLCO _dBWX _dDLC _erda
042			_apcc
050	0	0	_aHF5415.126 _b.P88 2012
082	0	0	_a658.40302855133 _223 _bP.D.C
100	1		_aPutler, Daniel S. _eauthor
245	1	0	_aCustomer and business analytics : _bapplied data mining for business decision making using R / _cDaniel S. Putler, Robert E. Krider.
264		1	_aBoca Raton, FL : _bCRC Press, _c[2012]
264		4	_a©2012.
300			_axxvi, 289 pages : _billustrations ; _c27 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
490	1		_aChapman & Hall/CRC the R series
504			_aIncludes bibliographical references (p. 283-285) and index.
505	0		_aI Purpose and Process Database Marketing and Data Mining Database MarketingData MiningLinking Methods to Marketing ApplicationsA Process Model for Data Mining-CRISP-DM History and Background The Basic Structure of CRISP-DMII Predictive Modeling Tools Basic Tools for Understanding Data Measurement Scales Software ToolsReading Data into R Tutorial Creating Simple Summary Statistics Tutorial Frequency Distributions and Histograms Tutorial Contingency Tables TutorialMultiple Linear Regression Jargon Clarification Graphical and Algebraic Representation of the Single Predictor ProblemMultiple RegressionSummary Data Visualization and Linear Regression TutorialLogistic RegressionA Graphical Illustration of the Problem The Generalized Linear Model Logistic Regression Details Logistic Regression TutorialLift Charts Constructing Lift Charts Using Lift Charts Lift Chart TutorialTree Models The Tree Algorithm Trees Models TutorialNeural Network Models The Biological Inspiration for Artificial Neural Networks Artificial Neural Networks as Predictive Models Neural Network Models TutorialPutting It All Together Stepwise Variable Selection The Rapid Model Development FrameworkApplying the Rapid Development Framework TutorialIII Grouping Methods Ward's Method of Cluster Analysis and Principal Components Summarizing Data Sets Ward's Method of Cluster Analysis Principal Components Ward's Method TutorialK-Centroids Partitioning Cluster Analysis How K-Centroid Clustering Works Cluster Types and the Nature of Customer Segments Methods to Assess Cluster Structure K-Centroids Clustering TutorialBibliography Index
520			_aCustomer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations. The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects. Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.
650		0	_aDatabase marketing _xSoftware.
650		0	_aData mining.
650		0	_aDecision making _xData processing. _919506
650		0	_aR (Computer program language)
650		0	_aDatabase management.
700	1		_aKrider, Robert E. _eco-author
830		0	_aChapman & Hall/CRC the R series.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK