Predictive Data Mining: A Practical Guide

The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles—and their practical manifestations—in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.

Note: If you already own Predictive Data Mining: A Practical Guide, please see ISBN 1-55860-477-4 to order the accompanying software. To order the book/software package, please see ISBN 1-55860-478-2.

+ Focuses on the preparation and organization of data and the development of an overall strategy for data mining.
+ Reviews sophisticated prediction methods that search for patterns in big data.
+ Describes how to accurately estimate future performance of proposed solutions.
+ Illustrates the data-mining process and its potential pitfalls through real-life case studies.

1 What is Data Mining?
2 Statistical Evaluation for Big Data
3 Preparing the Data
4 Data Reduction
5 Looking for Solutions
6 What's Best for Data Reduction and Mining?
7 Art or Science? Case Studies in Data Mining