Customer and business analytics : applied data mining for business decision making using R
Author(s)
Bibliographic Information
Customer and business analytics : applied data mining for business decision making using R
（The R series）（A Chapman & Hall book）
CRC Press, Taylor & Francis Group, c2012
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Note
Bibliography: p. 283-285
Includes index
Description and Table of Contents
Description
Customer 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.
Table of Contents
I Purpose and Process: Database Marketing and Data Mining. A Process Model for Data Mining-CRISP-DM. II Predictive Modeling Tools: Basic Tools for Understanding Data. Multiple Linear Regression. Logistic Regression. Lift Charts. Tree Models. Neural Network Models. Putting It All Together. III Grouping Methods: Ward's Method of Cluster Analysis and Principal Components. K-Centroids Partitioning Cluster Analysis. Bibliography. Index.
by "Nielsen BookData"