Customer and business analytics : applied data mining for business decision making using R - Putler, Daniel S.; Krider, Robert E. (Boca Raton, FL : CRC Press, 2015)
I Purpose and Process Database Marketing and Data Mining Database Marketing Data Mining Linking Methods to Marketing Applications A 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
Includes bibliographical references (pages. 283-285) and index.
CSE
Keywords: Database marketing -- Software; Data mining; Decision making -- Data processing; (Computer program language).; Database management; Computer science