"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.
Price: $62.99
User Reviews about Mastering Data Mining: The Art and Science of Customer Relationship Management
Although aged the book remains a precious guide for business and CRM people. It argues that data mining is a discipline that must be mastered by concentrating in how to approach analytics than how to use tools. Specifically it stresses the importance of:
- 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". -- A master piece
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. -- Great superficial knowledge but falls short overall
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. -- Ideas for GUI design of data mining software
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! -- Excellent book!
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. -- Good, not Great
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