Rating: 
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: Technically accurate and enjoyable to read
Review: The authors discuss data mining for marketing in a business context. Their descriptions of the techniques are clear and accurate, and the case studies provide excellent models. The book is very well written and has a comprehensive index.
Rating: 
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.
Rating: 
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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Summary: Learn how to write
Review: This book is chock full of the syntactic ambiguities which make relatively simple concepts, such as MBR, difficult to explore. e.g. "That is, 80 percent of the codes assigned by MBR were correct, but the cost was that 28 percent of the codes assigned were incorrect." Luckily, there's a picture explaining the mess. The ideas are here, but Berry and Linoff need to hire a real writer. I guess, smart folks like these guys have a tough time communicating with the common man? - Merv
Rating: 
Summary: Too many words and little content
Review: This book is very difficult to read. The authors say very little in hundreds of pages. I was looking for more content. The data mining techniques are explained in detail, but they are very difficult to understand. Perhaps this could also be because I am new to data mining. This is definitely not a book for someone looking for a quick introduction to data mining.
Rating:
Summary: Not really worth it if you have any clue
Review: This book labors to explain that Data Mining is possible. It spends 3 chapters explaining the business process flow of data mining as: 1. Figure out your business problem 2. data mine to figure out what to do 3. do it 4. measure to see if you did anything. It's nicely written, but VERY BASIC
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Summary: Too Much about nothing ....
Review: Verbose-Very Basic-Filled with unnecessary jargon, why?
Rating:
Summary: Not mathematical enough.
Review: Very disappointing, if you're looking for a mathematically oriented book. In fact it avoids math like the plague. It's therefore ideally suited to: (a) project managers who don't really want to do any serious work themselves, and (b) people who want to drop words at cocktail parties to impress people. Totally unsuited to the serious researcher.
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