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Data Mining with Microsoft® SQL Server™ 2000 Technical Reference (IT Professional) Hardcover – 25 May 2001
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According to Data Mining with Microsoft SQL Server 2000 Technical Reference, your organisational database is only as good as the strategic data you can extract from it. Do customers who buy breakfast cereal typically buy bananas as well? Is there a correlation between rainfall in a particular region and the prevalence of a particular illness there? Data Mining with Microsoft SQL Server 2000 Technical Reference shows how to use Microsoft's analysis tools for large databases. Author Claude Seidman offers advice on the data-modelling engineering process as a whole, including designing strategies likely to yield meaningful results, designing data warehouses, growing decision trees, spotting clusters and anomalies in data and automating mining processes with code.
Despite its designation as a reference, this book is largely a tutorial--you'll refer to it for advice on how to make Analysis Services do something in particular. Seidman uses a classic and effective tutorial technique, sticking with an example throughout the book and adding to previous examples as he explores additional aspects of Microsoft data mining. His illustration involves identifying edible mushrooms, based on a database of facts about known mushrooms, and he's combined how-to prose with screen shots and accumulated wisdom to great effect. If your organisation has gone with Microsoft SQL Server 2000 for data storage, read this book for advice on knowledge extraction. --David Wall
Topics covered: Microsoft Analysis Services, including the proper use of Data Transformation Services (DTS), PivotTable Services, Decision-Support Objects (DSO), and the Microsoft implementation of Online Analytical Processing (OLAP ).
From the Back Cover
With its state-of-the-art capabilities for rapidly processing and retrieving huge quantities of data, Microsoft(r) SQL Server 2000 is quickly growing in popularity among large corporations. But learning how to take advantage of the powerful, built-in data-mining services in SQL Server to turn all that data into meaningful information takes time and effort. Data Mining with SQL Server 2000 Technical Reference is the ideal, in-depth reference guide for any database developer, administrator, or IT professional who needs comprehensive information about these powerful new data-mining services. In particular, it fully examines the data-warehousing architecture in SQL Server 2000 to show how to take full advantage of the data-mining services in this RDBMS. This is the only Microsoft-approved technical guide to the data mining services in SQL Server 2000.See all Product description
Most helpful customer reviews on Amazon.com
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!
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.
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.
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