Predictive Data Mining: A Practical Guide (The Morgan Kaufmann Series in Data Management Systems)

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en Limba Engleză Paperback – 08 Dec 1997
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.
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ISBN-13: 9781558604032
ISBN-10: 1558604030
Pagini: 228
Ilustrații: black & white illustrations
Dimensiuni: 152 x 230 x 13 mm
Greutate: 0.4 kg
Seria The Morgan Kaufmann Series in Data Management Systems


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


"I enjoy reading PREDICTIVE DATA MINING. It presents an excellent perspective on the theory and practice of data mining. It can help educate statisticians to build alliances between statisticians and
data miners."
--Emanuel Parzen, Distinguished Professor of Statistics, Texas A&M University