Data Mining with SQL Server 2005 (Paperback)

by ZhaoHui Tang (Author), Jamie MacLennan (Author) "Data mining is getting more and more attention in today's business organizations..." (more)
Key Phrases: Microsoft Decision Trees, Star Wars Episode, Microsoft Neural Network (more...)

Editorial Reviews

Product Description
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

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

See all Editorial Reviews

Product Details

• Paperback: 480 pages
• Publisher: Wiley (October 7, 2005)
• Language: English
• ISBN-10: 0471462616
• ISBN-13: 978-0471462613
• Product Dimensions: 9.1 x 7.4 x 1.1 inches
• Shipping Weight: 1.5 pounds (View shipping rates and policies)
• Average Customer Review: 3.9 out of 5 stars See all reviews (15 customer reviews)
• Amazon.com Sales Rank: #476,739 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

26 of 28 people found the following review helpful:

3.0 out of 5 stars Useful Text, Marred By Poor Technical Editing and Typos, December 2, 2005

By	E. Qaisar "www.interviewboard.com" (New York, NY) - See all my reviews (REAL NAME)

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.

Chapter 1 - Introduction to Data Mining -quick intro to data mining, major vendors, project cycle

Chapter 2 - OLE DB for Data Mining - good coverage (despite the errors) of key concepts. You may be tempted to skip this chapter and dive into the "newer" stuff in Chapter 3 - I would urge you to understand chapter 2 before you move on.

Chapter 3 - Shows how to use the Business Intelligence Development Studio

Chapters 4 - 10 - Cover the various DM algorithms from Naïve Bayes to Neural Network. Includes both the general description of the algorithm as well as syntactical stuff as applied to SQL Server 2005.

Chapter 11 - Mining OLAP Cubes - fairly rudimentary stuff here.

Chapter 12 - Data Mining with SQL Server Integration Services (SSIS) - introduces SSIS and then covers some basic ground relating to DM tasks in SSIS.

Chapters 13 and 14 - SQL Server Data Mining Architecture and Programming

Chapter 15 - shows how to implement a simple web cross-selling application. Don't expect to implement Amazon like recommendations, but it is a good start anyway.

Chapter 16 - discusses using Excel for forecasting using Analysis services in the background

Chapter 17 - A brief (about 10 pages) text devoted to extending the DM framework using custom plug-in algorithms.

17 of 18 people found the following review helpful:

3.0 out of 5 stars Decent Book, March 16, 2006

By	Terry Smith "http://terrysmith.net -- http:/... (Little Rock, AR USA) - See all my reviews (REAL NAME)

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.

22 of 25 people found the following review helpful:

5.0 out of 5 stars This is Data Mining _with_ SQL Server 2005, February 1, 2006

By	C. J. Maclennan (Redmond, WA USA) - See all my reviews (REAL NAME)

I wouldn't normally review my own book, but I think the previous reviewer missed the point. This book is clearly about data mining in the context of the features of SQL Server 2005. It does not pretend to be anything else, and pretty much states so directly in the title. If you are looking for a generic, vendor neutral "all-up" book about data mining, its methods and applications, you're right, this book is not that book. However, if you want to learn about the data mining features of SQL Server 2005, how they work, how to use them and exploit them for your own analyses and applications, this is the book to read.