Data Mining with Microsoft SQL Server 2008

by ZhaoHui Tang, Bogdan Crivat, Jamie MacLennan

Book Overview

Eine praxisorientierte Einf?hrung in das Data Mining Toolset des SQL Server 2008 und die neuen Data Mining Add-Ins f?r Office 2007.

Enth?lt detaillierte Erl?uterungen und Beispiele zu allen neuen Data Mining Features des SQL Server 2008.

Gibt pr?zise Anleitungen zum Arbeiten mit den wichtigsten Data Mining-Algorithmen, (Naive Bayes-, Decision Trees-, Time Series-, Sequence Clustering-, Association- und Neural Network-Algorithmus),...

• Format: Paperback
• Language: English
• ISBN: 0470277742
• ISBN13: 9780470277744
• Release Date: October 2008
• Publisher: Wiley
• Length: 672 Pages
• Weight: 3.28 lbs.
• Dimensions: 1.3" x 7.5" x 9.3"

Customer Reviews

Data Mining with Microsoft SQL Server 2008

This is an excellent book for beginner to expert. Contains tons of relevant information. Could have been more detailed for the beginner, but authors did as well as possible without having to write a multi-volume version. Mid level to expert users should have no problem understanding the examples and solutions used in the book. I strongly recommend this book.

Get your hands dirty with SQL Server 2008 Data Mining Tools

This is an excellent hands-on book on learning core concepts of data mining with SQL Server 2008. I have used this book as part of our Data Mining certification course at University Of Washington. The examples are very clear and their step-by-step approach really helped me in understanding various mining models and learning DMX query. Highly recommend for aspirant and experienced BI professionals.

A superb book and must have

Jamie MacLennan, ZhaoHui Tang, and Bogdan Crivat have done a superb job on this book and for those of us that had to deal with the 2005 version rest assured dear reader that the 2008 version is a 'work of art'. The writing is clear and concise. The examples are easy to work with, understandable and gone are the complex mathmathics that required genious to interpret. I have bought a copy for myself and two for the office. I have a problem with recommending books for the sake of recommending them. Trust me the 2008 version is worth every cent!!. Thanks Jamie et Al and well done!!!!

The authoritative resource on SQL Server 2008 Data Mining

"Data Mining with Microsoft SQL 2008" is a great resource for learning how to perform data mining using SQL Server 2008 and Analysis Services 2008, with the added benefit that it is written by some of the people that influenced the design of the product. The presentation is clear and easy to read, and the content is rich in examples of using the Analysis Services data mining algorithms. Aspects such as how to create mining models, how to view them, and how to interpret results are covered in detail. An entire chapter is dedicated to creating plug-in algorithms, which is a powerful way of extending existing algorithms and implementing new solutions for a custom data mining problem. Another chapter covers mining data in Excel using the Data Mining Add-in for Excel, which I find to be very useful for experimenting with data sets and for quickly analyzing data. I recommend this book as a great source for gaining valuable knowledge if you are interested in Data Mining using SQL Server 2008 and Analysis Services 2008. Disclaimer: I worked on the development of Analysis Services 2008 and I have reviewed some of the book samples.

A plug-in developer must read

This book was a very useful resource while developing a plug-in algorithm to the SQL Server Data Mining framework. I have had mixed results in the past trying to integrate into the framework, but both this book and the previous SQL Server 2005 version answered all my questions and helped me and my company to create a much improved product offering. With a little bit of time vague requests like "build a recommendation engine" can be turned into crowd pleasers, without having to delve into custom algorithm development and extensive research. The price tag is a little steep, but the content is well worth it.