Editorial Review:
"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.
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Customer Reviews
Average Customer Rating: 
Great superficial knowledge but falls short overall  2004-12-08
Mastering Data Mining is a great book for quick superficial reference or a crash course in data mining but it becomes useless as more complicated issues araise. The book has a lot of practical examples and quick tips on the outside but as soon as you begin scratching the surface you find out that the examples are as general as they are vague. Some important points in model building are completely omitted and hidden with a graph or nice looking footnote.
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.
Ideas for GUI design of data mining software 2001-05-12
While doing a graduate elective on Decision Making Technologies, I realized that data visualization and representation is crucial for data exploratory and validation of data mining analysis. To get some ideas on how the various data visualization and workflow techniques are applied and integrated into the GUI of commercial softwares, survey the various chapters of this book.
Excellent book! 2000-06-19
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!
Good, not Great 2000-05-19
This book provides a number of case studies on applying data mining. I didn't learn a lot since the studies weren't applicable to what I am doing. Someone else might get more out of than me though. I did like their first book (it was very good) but this one wasn't nearly as good. There are better books that discuss the use of data mining software.
A book from practitioners 2000-03-30
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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