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Rating: 
Summary: Too many words and little content
Review: although I am a mathematician, this book would be unbored for me. it was written clearly, everybody that is interested in the concept of data mining can easily understand it. there is no so much complexity. Especially the link with the marketing was constructed strongly, I wanna advise this book for the business analysists, statisticians and marketers and ....
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Summary: Everyone Should Do This
Review: Data mining is such a simple thing that you wonder why more companies don't do a better job of mining their own data sitting on their own hard disks.
If a customer buys the first in a series of mystery novels, who better to send a note telling him that the second book is now available. That's the essence of data mining. This would allow you to get a much higher return on your mailing, saving money and increasing return on your marketing.
This is one of those books that you need to read every few months. Each time you go through it you will find some idea that will enable you to get more out of your data. It isn't a book heavy on programming, but on the concepts that have worked for others.
Highly recommended.
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Summary: Very good data mining book!!!
Review: I've purchased this book a week ago. So far I've read until chapter four and have learned a lot already. Descriptions are clear and the authors presented a lot of implementation scenarios/samples.
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Summary: Great Approach to DM Mixing Business topics w/ DM techniques
Review: It looks to me as a good book, because it mixes the strategic approach with data mining techniques. It begins defining what Data Mining is, main tasks, Data Mining stages and then it follows with techniques.The best is that there is some chapters that covers the same topics but one with theoretical approach and the next with examples for those previous topics. You really understand what this is about.This is specially great for non native english speakers as me. (spanish) I guess it requires some statistical knowledge and some systems modeling too. If readers forgot something about probabilities, hypothesis tests, etc... i recomend having some statistical book next to you. I guess is a good book for business college students as me, getting involved in this matter.
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Summary: Undirected Knowledge Discovery
Review: Once in a while, you run into a book that sheds new light into a subject that you thought you knew. This book redefined what data mining is for me. It also showed me how it fits into the bigger picture of enterprise business intelligence.I come from data warehousing background. I studied statistics and familiar with the techniques described. Until now, I regarded each topic as separate with its own functional applications. Now I realize that all these pieces come together in a single solution that maps to all business processes. Also the examples of easy to understand marketing applications got me started in identifying various processes that can benefit from it. Now I am only left with the details of implementation that I am eager to get started on.
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Summary: An Invitation from the Authors
Review: People interested in this book may also be interested in our web site at www.data-miners.com.
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Summary: Good Introduction book, not limited to Marketing
Review: The authors explain in a detailed way the most popular Data Mining techniques. The topics about Neuronal Networks, Decision Trees, Market-Basket Analysis and Memory-Based Reasoning are excellent. I think the topic Genetic Algorithms could be a bit more developed, but for the beginner is a good first overview. I have missed a topic about fuzzy logic. Given that the 90% of Data Mining projects are based on Marketing (1:1), the book is absolutely suitable for starting with these concepts, although I feel the book can be used in any other field (Just-In-Time Inventory, Demand Forecasting, Supply Value Chain, etc.)In my opinion, it was very useful for my work and I considered it as a reference book.
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Summary: Depends what you want this book for
Review: This book gives an overview of what data mining is and the tools available to perform it; Market Basket Analysis, Memory Based Reasoning, Automatic Cluster Detection, Link Analysis, Decision Trees, Artificial Neural Networks. Genetic Algorithms are also included, which, while not a data mining tool, are being used to train neural nets. In each case the authors describe the principles behind the tool, its strengths and weaknesses and applications were it is applicable. The authors give tips on what data preparation is required for the tool, both in terms of data "massaging", (which is required for neural nets) and indicate were it is important to select training sets that have approximately equal proportions of "good" & "bad" outcomes, in order for the tool to predict correctly. The descriptions include simple examples of the tool to give an overview of how the tool works. But as the title indicates, this book is for users who are considering using data mining tools. It does not describe how to use particular applications, neither does it include code examples (pseudo or actual) if you are interesting in developing your own tools. The book is easy to read and includes many examples from their experience of data mining in the real world.
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