- Paperback: 384 pages
- Publisher: Microsoft Press; 1 edition (22 May 2001)
- Language English
- ISBN-10: 0735612714
- ISBN-13: 978-0735612716
- Product Dimensions: 22.9 x 19.5 x 2.1 cm
- Amazon Bestsellers Rank: 2,116,396 in Books (See Top 100 in Books)
- See Complete Table of Contents
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Product Description
Amazon.co.uk Review
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 ).
Product Description
The amount of information stored in corporate databases is exploding exponentially. Data mining—finding meaningful patterns in all that data—can give any organization a competitive advantage. This book is the in-depth reference from Microsoft® for anyone who wants to take full advantage of the powerful data-mining features in SQL Server™ 2000. It examines the SQL Server 2000 Analysis Services architecture and shows how data mining fits into its complete suite of information-extraction technologies. Then it demonstrates how to structure and mine large databases with the algorithms included with SQL Server 2000 to find nuggets of useful information. It even shows how to create a practice data-mining model using data downloaded from a database. Coverage includes:
- INTRODUCTION TO DATA MINING: What data mining is and isn’t, plus important principles and definitions behind data-mining methodologies, including the role of data-mining models, statistics, and algorithms
- SQL SERVER 2000 ARCHITECTURE: How data mining fits into the SQL Server 2000 Analysis Services architecture and how it builds on the SQL Server 2000 relational database and its embedded online analytical processing (OLAP) engine
- DATA-MINING METHODS: How to choose the best data-mining method for the job—decision trees or clustering
- EASE OF USE FEATURES: How to use the Mining Model Wizard and the OLAP Mining Model Editor to simplify creating, training, and processing a model
- PROGRAMMING THE DATA-MINING SERVICES: How to use data-mining models and Data Transformation Services, PivotTable® Services, decision-support objects (DSO), PERL, Visual Basic®, Scripting Edition, XML, and other tools and languages to work with the data-mining engine
Inside This Book
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Recently I spoke with the CEO and CIO of a major auto sales company about their databases. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Recently I spoke with the CEO and CIO of a major auto sales company about their databases. Read the first page
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7 reviews
17 of 17 people found the following review helpful
Book is better than the product
3 Aug 2001
Format:Paperback
I have to agree with one of the previous reviewers when he said that given the absence of practically *ANY* documentation provided by Microsoft, this book is your only real source of information about Microsoft's data mining product.
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!
11 of 11 people found the following review helpful
Working in a Data Mine
13 Jun 2001
Format:Paperback
I always look for Seidman's contributions in SQL magazines and at conferences since he always has something interesting and innovative to say that is fuelled by his depth of knowledge of the subject. What's more he has a great knack of presenting complicated (and sometimes, let's face it, not very exciting) concepts in a way that makes them easy to understand and in contexts that anyone can relate to. In particular his style is reminiscient of Roger Sessions (Com+ and the Battle for the Middle Tier, etc) minus the cynicism but with equal enthusiasm. No book can be all things to all people, but this is an excellent introduction to the world of Data mining and the power behind SQL200's implementation of it. I would recommend it to anyone looking to discover those hidden trends and patterns in their data, exploit them and become their CFO's best friend.
8 of 9 people found the following review helpful
Good technical reference
13 July 2001
Format:Paperback
A lot of the information found in this book should have already been in the online documentation. The lack of documentation for data mining under sql server 2000 makes this book the only usefull reference out there. But overall, the book is poorly organised, badly written and requires a lot more in-dept information in order to put data mining into practical use.
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.
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