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Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R (Chapman &Hall/CRC the R Series) [Paperback]

Daniel S. Putler , Robert E. Krider

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Table of Contents

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

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