Predictive Data Mining: A Practical Guide - Brossura

Weiss, Sholom M.; Indurkhya, Nitin

Brossura

ISBN 10: 1558604030 ISBN 13: 9781558604032

Casa editrice: Morgan Kaufmann Pub, 1997

Riassunto

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.

Su questo libro

Informazioni sugli autori

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.

Dalla quarta di copertina

Note: If you already own Predictive Data Mining: A Practical Guide, please click here to order the accompanying software. To order the book/software package, please click here.

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.

"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|Note: If you already own Predictive Data Mining: A Practical Guide, please click here to order the accompanying software. To order the book/software package, please click here.

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.

"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

Dettagli dell'edizione

• Editore: Morgan Kaufmann Pub
• Data di pubblicazione: 1997
• Lingua: Inglese
• ISBN 10: 1558604030
• ISBN 13: 9781558604032
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 228
• Contatto del produttore: Elsevier GmbH; https://www.elsevier.com/; ; Jägerstraße 41; Berlin; Berlin; 10117; Germania