Data Mining with SQL Server 2005

Features

• Cover Type: Paperback with 480 pages
• Published by: Wiley October 7, 2005
• Written in: English
• ISBN 10 Number: 0471462616
• ISBN 13 Number: 978-0471462613
• Book Dimensions: 9.1 x 7.4 x 1.1 inches
• Weighs: 1.5 pounds

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

Back Cover Copy
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

Reader Reviews
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 - ten - 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 ten pages) text devoted to extending the DM framework using custom plug-in algorithms.