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Rating: 
Summary: Good technical reference
Review: A lot of the information found in this book should have already been in the online documentation. The lack of documentation for data mining under sql server 2000 makes this book the only usefull reference out there. But overall, the book is poorly organised, badly written and requires a lot more in-dept information in order to put data mining into practical use.You will find some information on DTS, but there are much better books out there on the topic. You will find some sample code for using DSO, but this topic is only touched upon and the code is NOT explained very well. The most important chapters were very thin (programming data mining and data mining queries). After reading the book, you will have an introduction to data mining, but you won't be able to use it effectively. The examples in the book have no commercial value and are completely worthless. There is no CDROM that contains the data the author is using, and the sample data on the web is different to the data in the book. You will also have to start with chapter 8 (DTS) to load the sample data before you can follow the examples in the book. I was really looking forward to get a copy of this book, but now that I have a copy, I am very dissapointed. The contents of this book shows that the author has no real world experience on the topic or is not willing to share it.
Rating: 
Summary: Working in a Data Mine
Review: I always look for Seidman's contributions in SQL magazines and at conferences since he always has something interesting and innovative to say that is fuelled by his depth of knowledge of the subject. What's more he has a great knack of presenting complicated (and sometimes, let's face it, not very exciting) concepts in a way that makes them easy to understand and in contexts that anyone can relate to. In particular his style is reminiscient of Roger Sessions (Com+ and the Battle for the Middle Tier, etc) minus the cynicism but with equal enthusiasm. No book can be all things to all people, but this is an excellent introduction to the world of Data mining and the power behind SQL200's implementation of it. I would recommend it to anyone looking to discover those hidden trends and patterns in their data, exploit them and become their CFO's best friend.
Rating:
Summary: Extremely useful
Review: I found this book to the most helpful resource on MS data mining out there today. I was afraid I'd find a rehash of the Microsoft help files, but was pleasantly surprised to find lots of new, original information. There's lot's of dicussion of process as well as step-by-step instructions on the use of the data mining product. Very good explanations as to the use of DSO and DTS to create models and to query them.
Rating: 
Summary: Don't bother
Review: I found this offering terribly wanting; illustrating concepts with material (the mushrooms database) that was not available without a significant amount of effort -- no CD or website with sample data. In my opinion, this book included way too much VB code of questionable value. If you wnat to learn about data mining, look elsewhere. If you want to use the Microsoft code libraries to manipulate the analysis services object models, this book might be helpful. It seems to me that the author could have done a better job with the statistical concepts. Noteworthy are the formulae for standard deviations (p.328) which just did not seem to jibe with the actual calculations in the example.
Rating: 
Summary: Book is better than the product
Review: I have to agree with one of the previous reviewers when he said that given the absence of practically *ANY* documentation provided by Microsoft, this book is your only real source of information about Microsoft's data mining product. I'm a big fan of OLAP amd data mining which made me better appreciate the time the author took to lay the groundwork for the discipline of data mining. Unlike a previous reviewer, I think that the author shares lots of real-world evperience which you can see by the way he bring up problems (which I have encountered myself) that occur when moving from raw data to a data mining model. He also catches some glitches and unreported features in the product for you and shows you how to work around them. The book is actually very complete considering that the data mining product put out by Microsoft is promising, but extremely rudimentary. It provides only two basic data mining algorithms and gives a very clumsy way to try to add other algorithms. Thankfully, the author discusses techniques and pitfalls of mining numerical data and even shows you how to use SQL Server 2000 to perform a regression analysis for that purpose. I would have given this book five stars except for two points : 1: The mushroom database is a good illustration of the use of the decision tree algorithm, but I think it may have been good to include a more business-oriented example that would bring data mining closer to it's intended purpose. 2: I was a little disappointed not to see any explanation as to how to add your own algorithms to the data mining product. Even if doing so requires C++ experience, it would have been perfectly fine to include it in a separate chapter or in an appendix. I don't know why the author chose not to include it. Byond that, I would definitely recommend this book if you need to use MS data mining. The book is well written, and considering the infancy of the product, it's also very complete. Besides, you have no other real resource out there!
Rating: 
Summary: Very Basic
Review: If you don't know ANYTHING about SQL/Analysis Server and need to learn about its implementation of data mining, you'll enjoy this book. In addition to data mining there's a fair amount of discussion on DSO, DTS, data warehousing, etc. (Of course, if don't anything about these technologies jumping into Analysis Server's implementation of data mining might be a bit premature.) But, if you are familiar with Analysis Server and data warehousing and want to learn a little data mining, then there's about a hundred pages of good reading. Seidman covers the basics of the two data mining technologies Microsoft implemented - decision trees & clustering. Finally, be prepared for some serious grunt work if you like to "read and code". The author does not include any helpful downloads to get going quickly.
Rating:
Summary: Don't bother
Review: This book just likes an academic seminar paper. It's hard to understand. It's not a practical book for study data mining.
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