Mastering Data Mining: The Art and Science of Customer Relationship Management [Englisch] [Taschenbuch]

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!

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!

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.