Mastering Data Mining: The Art and Science of Customer Relationship Management (Paperback)

~ (Author), Gordon S. Linoff (Author) "To say that the past century saw rapid change is both a cliche and an understatement..." (more)
Key Phrases: adding derived variables, undirected data mining, churn modeling, United States, Time Inc, Enterprise Miner (more...)

Editorial Reviews

Review

"I give it full marks for both content and value for money..."(Computer Bulletin - Book of the month, March 2001)

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.

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Product Details

• Paperback: 512 pages
• Publisher: Wiley; 1 edition (December 28, 1999)
• Language: English
• ISBN-10: 0471331236
• ISBN-13: 978-0471331230
• Product Dimensions: 9.1 x 7.3 x 1.4 inches
• Shipping Weight: 2.3 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (7 customer reviews)
• Amazon.com Sales Rank: #43,782 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

31 of 32 people found the following review helpful:

4.0 out of 5 stars A book from practitioners, March 30, 2000

By	Ron Kohavi (Lakemont, WA) - See all my reviews (REAL NAME)

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.

23 of 25 people found the following review helpful:

4.0 out of 5 stars Excellent book!, June 19, 2000

By	Michael J. Gaffney (Washington, DC) - See all my reviews

This book is an excellent book. The authors explain the various techniques, and show real world examples of their use. Most importantly, they explain the underlying goals of the various techniques, and what to watch out for when using them. I was most relieved to read that I am not alone in having limited success with association rules!

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!

5 of 5 people found the following review helpful:

5.0 out of 5 stars Highly Recommended, March 29, 2000

By	K. Thearling (Richmond, VA) - See all my reviews (REAL NAME)

Michael and Gordon are two of the best data mining consultants in the business and in this book they provide a thorough description of how to turn the technology into action. This book expands on their fantastic first book (Data Mining Techniques) with a wide variety of case studies and insight into what it takes to use data mining to impact real business problems. Highly recommended.