Items related to Predictive Data Mining: A Practical Guide (with Software)
Synopsis
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.
A state-of-the-art data-mining software kit accompanies the book. The software, which is delivered through a special web site, is a collection of routines for efficient mining of big data. Both classical and the more computationally expensive state-of-the-art prediction methods are included. Using a standard spreadsheet data format, this kit implements all of the data-mining tasks described in the book. The software is available for Windows 95/NT and Unix platforms (no need to specify when ordering).
* 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.
"synopsis" may belong to another edition of this title.
About the Author
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.
From the Back Cover
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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.
- 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.
A state-of-the-art data-mining software kit accompanies the book. The software, which is delivered through a special web site, is a collection of routines for efficient mining of big data. Both classical and the more computationally expensive state-of-the-art prediction methods are included. Using a standard spreadsheet data format, this kit implements all of the data-mining tasks described in the book. The software is available for Windows 95/NT and Unix platforms (no need to specify when ordering).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 this title" may belong to another edition of this title.
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