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Data Mining with SQL Server 2005 1st Edition

3.8 out of 5 stars 16 customer reviews
ISBN-13: 978-0471462613
ISBN-10: 0471462616
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Editorial Reviews

From the Back Cover

Your in-depth guide to using the new Microsoft data mining standard to solve today's business problems

Concealed inside your data warehouse and data marts is a wealth of valuable information just waiting to be discovered. All you need are the right tools to extract that information and put it to use. Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets. The authors explore the core concepts of data mining as well as the latest trends. They then reveal the best practices in the field, utilizing the innovative features of SQL Server 2005 so that you can begin building your own successful data mining projects.

You'll learn:

  • The principal concepts of data mining
  • How to work with the data mining algorithms included in SQL Server data mining
  • How to use DMX—the data mining query language
  • The XML for Analysis API
  • The architecture of the SQL Server 2005 data mining component
  • How to extend the SQL Server 2005 data mining platform by plugging in your own algorithms
  • How to implement a data mining project using SQL Server Integration Services
  • How to mine an OLAP cube
  • How to build an online retail site with cross-selling features
  • How to access SQL Server 2005 data mining features programmatically

About the Author

ZhaoHui Tang is a Lead Program Manager in the Microsoft SQL Server Data Mining team. Joining Microsoft in 1999, he has been working on designing the data mining features of SQL Server 2000 and SQL Server 2005. He has spoken in many academic and industrial conferences including VLDB, KDD, TechED, PASS, etc. He has published a number of articles for database and data mining journals. Prior to Microsoft, he worked as a researcher at INRIA and Prism lab in Paris and led a team performing data-mining projects at Sema Group. He got his Ph.D. from the University of Versailles, France in 1996.

Jamie MacLennan is the Development Lead for the Data Mining Engine in SQL Server. He has been designing and implementing data mining functionality in collaboration with Microsoft Research since he joined Microsoft in 1999. In addition to developing the product, he regularly speaks on data mining at conferences worldwide, writes papers and articles about SQL Server Data Mining, and maintains data mining community sites. Prior to joining Microsoft, Jamie worked at Landmark Graphics, Inc. (division of Halliburton) on oil & gas exploration software and at Micrografx, Inc. on flowcharting and presentation graphics software. He studied undergraduate computer science at Cornell University.


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Product Details

  • Paperback: 480 pages
  • Publisher: Wiley; 1 edition (October 7, 2005)
  • Language: English
  • ISBN-10: 0471462616
  • ISBN-13: 978-0471462613
  • Product Dimensions: 7.4 x 1 x 9.2 inches
  • Shipping Weight: 1.5 pounds
  • Average Customer Review: 3.8 out of 5 stars  See all reviews (16 customer reviews)
  • Amazon Best Sellers Rank: #2,921,967 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By E. Qaisar on December 2, 2005
Format: Paperback Verified Purchase
This could have been a 5-star book were it not for the numerous editing oversights. Note that I say "editing oversights" - I don't really blame the authors as they have the right credentials for tackling this subject.

Examples of editorial omissions:

1) Page 45 - the text says that the MemberCard_Prediction mining model uses Gender, Age, Profession, HasChildren and HouseOwner to predict the membership card type. However, in the definition of mining model itself, HasChildren attribute is missing.

2) Page 46 - same mistake as #1

3) Page 50 - poor editing - "the result rowsets has the structure displayed in Figure 2.4". Great. Now where exactly is Figure 2.4? You have to flip pages in the book back to page 41 to find it. How much effort would it have taken for the editors to rephrase the sentence to "the result rowsets has the structure displayed in Figure 2.4 on page 41."?

3) Page 51 - not really a mistake, but poor editing anyway - "In Figure 2.7, the table on the right is a truth table. The left table is a new......". Now, when you look at figure 2.7 on page 52 and see the way tables are arranged, it would have been better to say "The bottom table is a truth table and the top table is a new....).

4) Page 53 - "A Prediction Query Example" - the Select statement refers to M.MemberCard, but "M" itself not listed as an alias in the From clause.

Ok...I think you get the idea. Now to the nice stuff:

It you are interested in data mining with SQL Server 2005, this is still a book you must have. Those with an understanding of data mining principles will benefit most. In addition, you may need to brush up on statistics to really understand what is going on.
Read more ›
Comment 28 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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Format: Paperback
At the time I'm writing this review this book is the only one available dedicated solely to the data mining features of SQL Server 2005. The book is good, but I was disappointed in it as well on three fronts. First, there is a chapter dedicated to each of the data mining algorithms. I didn't find the business use case examples for when, why, and how to use each algorithm sufficient. Second, each of the algorithm chapters goes off the deep-end explaining the mathematical basis for the algorithm. There are very, very few developers who are going to remember enough of their college mathematics to follow along. Third, the technical coverage of how to use the APIs and the data mining extension language (DMX) is superficial, particularly with DMX. After reading this book cover to cover I couldn't go off and write a DMX query if I wanted to. On the application I'm working on we are planning to implement our own web visualization viewers for the data mining algorithms. This book didn't give me what I needed in understanding the object model exposed by the APIs in order to handle the back-end coding for this. All in all, if you are planning to do data mining with SQL Server 2005 I would recommend this book only because at the time of this writing there is nothing else available. However, you will learn quite a bit about data mining with it and depending on your prior experience (more is better) it might be an excellent fit for you.
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Format: Paperback
Another reviewer mentioned poor technical editing. My impression was that the code samples perhaps had a few syntax errors and wouldn't run without a bit of editing, and that the reviewer was probably being overpicky.

My impression was wrong! This is the worst edited book I've ever read. There are mistakes throughout that seriously distract the reader from the content.

This is unfortunate, because I thought the content was very good. If you are a statistically training data analyst, it might not go into enough depth, but if you are a database developer looking to bring data mining to your business/applications, this provides excellent coverage, from how to create the models to good descriptions and comparisons of the different algorithms SQL 2005 offers.
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Some people commented on the poor editing: typos & some wrong pictures. True. (It detracts, but you can figure it out easily)

Some stated that it is not a good general overview of Data Mining. True (though it has a bit of a summary)

Some stated that is doesn't discuss business applications in detail. Yes, (it only makes brief reference to them).

Some stated that it is very vendor specific. Hello, read the title - SQL 2005.

It is a must read for anyone who wants to take maximum advantage of SQL Server 2005 Data Mining. It goes thru all the algorithms, tells you how each one works, how to tune them & how to embed them into your applications. It compliments the Books On-Line materials, tutorials & sample code that ship with the product.

(interesting how people pay for a textbook & never bother to read the copous amount of materials that ships with the product.)

It does give you a bit of background in DM, & does walk you thru using the tools (SSMS & BIDS) used to create & administer the Data Mining.

It doesn't talk about using the Data Mining Viewer controls in Visual Studio 2005.

It is an easy read & very informative. Especially if you go to the trouble of downloading the samples & data from the web site & actually build the models with the book & step thru the code.

While it isn't really an indepth treatment of DMX in the way that "George Spofford's MDX Solutions" is for MDX. It does give you more than enough examples to be able to create, train & predict from the models.

It also gives enough to embed your DM models into your applications, Use them from Excel & take full advantage of the DM built-into SQL Intergration Services.

If you want an DM Overview for business use - check out Barry Lindof's book
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