Microsoft Data Mining

Name: Microsoft Data Mining
Brand: DIGITAL PR
Price: 192.75 AUD

Integrated Business Intelligence for E-Commerce and Knowledge Management

By: Barry de Ville, Peter K. MacKinnon (Foreword by)

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Paperback | 1 April 2001

At a Glance

Paperback
336 Pages

Dimensions(cm)
24.13 x 18.42 x 1.91

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$192.75

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Microsoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes.

Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management.

Unique book on a hot data management topic
Part of Digital Press's SQL Server and data mining clusters
Author is an expert on both traditional and Microsoft data mining technologies

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Computer Programming & Software Development Object-Oriented Programming or OOP Data Capture & Analysis Database Programming

Foreword	p. xi
Preface	p. xiii
Acknowledgments	p. xix
Introduction to Data Mining	p. 1
Something old, something new	p. 3
Microsoft's approach to developing the right set of tools	p. 7
Benefits of data mining	p. 10
Microsoft's entry into data mining	p. 18
Concept of operations	p. 19
The Data Mining Process	p. 23
Best practices in knowledge discovery in databases	p. 24
The scientific method and the paradigms that come with it	p. 25
How to develop your paradigm	p. 30
The data mining process methodology	p. 37
Business understanding	p. 39
Data understanding	p. 41
Data preparation	p. 44
Modeling	p. 45
Evaluation	p. 49
Deployment	p. 51
Performance measurement	p. 54
Collaborative data mining: the confluence of data mining and knowledge management	p. 55
Data Mining Tools and Techniques	p. 59
Microsoft's entry into data mining	p. 60
The Microsoft data mining perspective	p. 60
Data mining and exploration (DMX) projects	p. 64
OLE DB for data mining architecture	p. 65
The Microsoft data warehousing framework and alliance	p. 71
Data mining tasks supported by SQL Server 2000 Analysis Services	p. 72
Other elements of the Microsoft data mining strategy	p. 86
Managing the Data Mining Project	p. 93
The mining mart	p. 94
Unit of analysis	p. 95
Defining the level of aggregation	p. 97
Defining metadata	p. 98
Calculations	p. 99
Standardized values	p. 102
Transformations for discrete values	p. 103
Aggregates	p. 103
Enrichments	p. 111
Example process (target marketing)	p. 112
The data mart	p. 115
Modeling Data	p. 117
The database	p. 118
Problem scenario	p. 118
Setting up analysis services	p. 120
Defining the OLAP cube	p. 124
Adding to the dimensional representation	p. 132
Building the analysis view for data mining	p. 135
Setting up the data mining analysis	p. 137
Predictive modeling (classification) tasks	p. 139
Creating the mining model	p. 141
The tree navigator	p. 147
Clustering (creating segments) with cluster analysis	p. 151
Confirming the model through validation	p. 158
Summary	p. 159
Deploying the Results	p. 163
Deployments for predictive tasks (classification)	p. 164
Lift charts	p. 172
Backing up and restoring databases	p. 175
The Discovery and Delivery of Knowledge for Effective Enterprise Outcomes: Knowledge Management	p. 177
The role of implicit and explicit knowledge	p. 179
A primer on knowledge management	p. 180
The Microsoft technology-enabling framework	p. 199
Summary	p. 208
Glossary	p. 213
References	p. 219
Web Sites	p. 223
Data Mining and Knowledge Discovery Data Sets in the Public Domain	p. 229
Microsoft Solution Providers	p. 255
Summary of Knowledge Management Case Studies and Web Locations	p. 289
Index	p. 301
Table of Contents provided by Syndetics. All Rights Reserved.

Microsoft Data Mining

Integrated Business Intelligence for E-Commerce and Knowledge Management

At a Glance

Paperback

Shipping

How to return your order

Defective items

You Can Find This Book In

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