- Paperback: 512 pages
- Publisher: Wiley; 1 edition (December 28, 1999)
- Language: English
- ISBN-10: 0471331236
- ISBN-13: 978-0471331230
- Product Dimensions: 7.4 x 1.1 x 9.3 inches
- Shipping Weight: 2.3 pounds
- Average Customer Review: 8 customer reviews
- Amazon Best Sellers Rank: #1,833,290 in Books (See Top 100 in Books)
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Mastering Data Mining: The Art and Science of Customer Relationship Management 1st Edition
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"I give it full marks for both content and value for money..."(Computer Bulletin - Book of the month, March 2001)
From the Back Cover
"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 loyalty
* Target the right prospects
* Identify new markets for products and services
* Recognize 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 providers
* Information on data mining conferences, courses, and other sources of information
* Full-color versions of the illustrations used in the book
Try the Kindle edition and experience these great reading features:
Showing 1-6 of 8 reviews
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Eric Siegel, Ph.D.
Founder, Predictive Analytics World
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.
Although some of the particular examples were not the type of examples I deal with, the reasons they were chosen make perfect sense. Data mining owes much of its popularity to people attempting to find churners, etc. But there are plenty of examples covered, and with each one some new insight is revealed. Especially useful to me were the explanations of what it is one sees in the decision trees, lift curves, etc. Also, seeing various problems solved with several of the popular tools (MineSet, Enterprise Miner, etc.) was very helpful. There are many examples from various industries, and you learn something new about those industries too! (If you like the Sesame Street videos of how cans, tires, etc. are made even more than your kids do, you'll love this book for the examples alone.)
It is clear from this book that the authors not only know what they are talking about, they can actually break it down for a newbie like me. I have also had the pleasure of being in one of Mr. Berry's MineSet classes, and he demonstrated the same depth of knowledge and ability to convey it to others in that class as well.
This book is not an algorithm book, but it touches on them. It is not necessarily a tour of data mining tools, but does do this to some degree. It is probably most useful for anyone who wants to know "What is this 'data mining', and how can it help me?" with real world examples to make things clear. If the reader starts out thinking that data mining is just tossing a bunch of data into a tool and getting concrete results back, the confusion will not remain after reading this book. Finally, this book is VERY easy reading. Do yourself (or your boss) a favor and buy this book!
- the iterative nature of data mining activities (and project life cycle)
- the active involvement of business people
- the business objectives and needs
- the preparation and split of the model set (mining view)
- the evaluation of produced models and patterns
- the business interpretation of data mining results
- the power of data exploration (by example)
"Mastering Data Mining" is a much more concrete and comprehensive book than "Data Mining Techniques, 2nd edition".
More than once I finished a chapter wondering how some model or technique was used. I would suggest reading only the first eight chapters which are a great introduction to overall data mining and skip the case studies. If you are expecting a more serious and detailed reading on data mining, look somewhere else because you won't find it here.