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Predictive Data Mining Sholom M. Weiss

Predictive Data Mining By Sholom M. Weiss

Predictive Data Mining by Sholom M. Weiss

Reviews:
$15.49
Condition - Very Good
Only 1 left
Very Good

Summary

A guide to developing data-mining applications. It offers advice on performing these large-scale, open-ended analyses for real-world data warehouses. It focuses on the preparation and organization of data and the development of an overall strategy for data mining. It reviews sophisticated prediction methods that search for patterns in big data.

Predictive Data Mining Summary

Predictive Data Mining: A Practical Guide by Sholom M. Weiss

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.

Predictive Data Mining Reviews

"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

About Sholom M. Weiss

Sholom M. Weiss is a professor of computer science at Rutgers University and the author of dozens of research papers on data mining and knowledge-based systems. He is a fellow of the American Association for Artificial Intelligence, serves on numerous editorial boards of scientific journals, and has consulted widely on the commercial application of advanced data mining techniques. He is the author, with Casimir Kulikowski, of Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, which is also available from Morgan Kaufmann Publishers. Nitin Indurkhya is on the faculty at the Basser Department of Computer Science, University of Sydney, Australia. He has published extensively on Data Mining and Machine Learning and has considerable experience with industrial data-mining applications in Australia, Japan and the USA.

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

Additional information

GOR004940471
9781558604032
1558604030
Predictive Data Mining: A Practical Guide by Sholom M. Weiss
Sholom M. Weiss
The Morgan Kaufmann Series in Data Management Systems
Used - Very Good
Paperback
Elsevier Science & Technology
1997-12-08
228
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in very good condition, but if you are not entirely satisfied please get in touch with us

Customer Reviews - Predictive Data Mining