Customer and business analytics : applied data mining for business decision making using R /
Daniel S. Putler and Robert E. Krider
- Boca Raton CRC Press 2012
- 289 p.
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-DM II Predictive Modeling Tools Basic Tools for Understanding Data Measurement Scales Software Tools Reading Data into R Tutorial Creating Simple Summary Statistics Tutorial Frequency Distributions and Histograms Tutorial Contingency Tables Tutorial Multiple Linear Regression Jargon Clarification Graphical and Algebraic Representation of the Single Predictor Problem Multiple Regression Summary Data Visualization and Linear Regression Tutorial Logistic Regression A Graphical Illustration of the Problem The Generalized Linear Model Logistic Regression Details Logistic Regression Tutorial Lift Charts Constructing Lift Charts Using Lift Charts Lift Chart Tutorial Tree Models The Tree Algorithm Trees Models Tutorial Neural Network Models The Biological Inspiration for Artificial Neural Networks Artificial Neural Networks as Predictive Models Neural Network Models Tutorial Putting It All Together Stepwise Variable Selection The Rapid Model Development Framework Applying the Rapid Development Framework Tutorial III 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 Tutorial K-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 Tutorial Bibliography Index
9781466503960
Data Mining R (Computer program language) Database Management Decision making--Data processing Database marketing