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Predictive Data Mining: A Practical Guide / Edition 1
     

Predictive Data Mining: A Practical Guide / Edition 1

by Sholom M. Weiss, Nitin Indurkhya
 

ISBN-10: 1558604030

ISBN-13: 9781558604032

Pub. Date: 08/15/1997

Publisher: Elsevier Science

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

Overview

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.

Product Details

ISBN-13:
9781558604032
Publisher:
Elsevier Science
Publication date:
08/15/1997
Series:
Morgan Kaufmann Series in Data Management Systems Series
Pages:
228
Product dimensions:
0.55(w) x 6.00(h) x 9.00(d)

Table of Contents

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

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