Features
- Cover Type: Paperback with 512 pages
- Published by: Wiley
- Edition: 1st Edition December 28, 1999
- Written in: English
- ISBN 10 Number: 0471331236
- ISBN 13 Number: 978-0471331230
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Book Dimensions:
9.1 x 7.3 x 1.2 inches
- Weighs: 1.7 pounds
Product Review
"I give it full marks for both content and value for money"(Computer Bulletin - Book of the month, March 2001)
Product Description
"Berry and Linoff lead the reader down an enlightened path of best practices." -Dr. Jim Goodnight, President and Cofounder, SAS Institute Inc.
"This is a great book, and it will be in my stack of four or five essential resources for my professional work." -Ralph Kimball, Author of The Data Warehouse Lifecycle Toolkit
Mastering Data Mining
In this follow-up to their successful first book, Data Mining Techniques, Michael J. A. Berry and Gordon S. Linoff offer a case study-based guide to best practices in commercial data mining. Their first book acquainted you with the new generation of data mining tools and techniques and showed you how to use them to make better business decisions. Mastering Data Mining shifts the focus from understanding data mining techniques to achieving business results, placing particular emphasis on customer relationship management.
In this book, you'll learn how to apply data mining techniques to solve practical business problems. After providing the fundamental principles of data mining and customer relationship management, Berry and Linoff share the lessons they have learned through a series of warts-and-all case studies drawn from their experience in a variety of industries, including e-commerce, banking, cataloging, retailing, and telecommunications.
Through the cases, you will learn how to formulate the business problem, analyze the data, evaluate the results, and utilize this information for similar business problems in different industries.
Berry and Linoff show you how to use data mining to:Retain customer loyaltyTarget the right prospectsIdentify new markets for products and servicesRecognize cross-selling opportunities on and off the Web
The companion Web site at http://www.data-miners.com features:Updated information on data mining products and service providersInformation on data mining conferences, courses, and other sources of informationFull-color versions of the illustrations used in the book.
Reader Reviews
Many books have been written on the algorithms used for data mining (e.g., machine learning, statistics). This is not yet another one. This book is geared at people who want to derive insight and take action in a business setting. It is now well known that the algorithmic step is only a small part of the iterative knowledge discovery process, yet few books enlighten the users with the issues involved. This book has a small section on the algorithms, but concentrates on the often-overlooked PROCESS of data mining (sometimes called knowledge discovery) and the problems associated with this process in practice. Michael and Gordon are practitioners who have used multiple data mining tools and techniques. They know the problems and describe them well, sharing their real-life experiences through actual case studies. For example, people rarely appreciate the main problem with association algorithms: the number of uninteresting rules they generate. Now I can show them pages 426-428. The few things that I didn't like were the use of non-standard terminology in a few cases. For example, directed instead of supervised; prediction instead of regression. While the common terms aren't great, they're standard now. The book also has few references. Someone readers will want to read more details about specific areas and will not find needed references. Overall, it's a well written book, easy to read, with nice analogies to the world of photography.
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