Microsoft data mining : integrated business intelligence for e-Commerce and knowledge management Barry de Ville.

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Microsoft data mining : integrated business intelligence for e-Commerce and knowledge management Barry de Ville.



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Book's title: Microsoft data mining : integrated business intelligence for e-Commerce and knowledge management Barry de Ville.
Library of Congress Control Number: 00047514
International Standard Book Number (ISBN):1555582427 (pbk. : alk. paper)
Cataloging Source:DLC, DLC DLC
Authentication Code:pcc
Library of Congress Call Number:QA76.9.D343 D43 2001
Dewey Decimal Classification Number:006.3$221
Personal Name:De Ville, Barry.
Publication, Distribution, etc.:Boston . Digital Press, (c)2001.
Physical Description:xx, 315 : ill. ;, 24 cm.
General Note:Includes index.
Formatted Contents Note:Machine generated contents note:
I Introduction to Data Mining
I.I Something old, something new
1.2 Microsoft's approach to developing the right set of tools
1.3 Benefits of data mining
1.4 Microsoft's entry into data mining
1.5 Concept of operations
2 The Data Mining Process
2.1 Best practices in knowledge discovery in databases
2.2 The scientific method and the paradigms that come with it
2.3 How to develop your paradigm
2.4 The data mining process methodology
2.5 Business understanding
2.6 Data understanding
2.7 Data preparation
2.8 Modeling
2.9 Evaluation
2.10 Deployment
2.11 Performance measurement
2.12 Collaborative data mining: the confluence of data mining
and knowledge management
3 Data Mining Tools and Techniques
3.1 Microsoft's entry into data mining
3.2 The Microsoft data mining perspective
3.3 Data mining and exploration (DMX) projects
3.4 OLE DB for data mining architecture
3.5 The Microsoft data warehousing framework and allian(
3.6 Data mining tasks supported by SQL Server 2000
Analysis Services
3.7 Other elements of the Microsoft data mining strategy
4 Managing the Data Mining Project
4.1 The mining mart
4.2 Unit of analysis
4.3 Defining the level of aggregation
4.4 Defining metadata
4.5 Calculations
4.6 Standardized values
4.7 Transformations for discrete values
4.8 Aggregates
4.9 Enrichments
4.10 Example process (target marketing)
4.11 The data mart
5 Modeling Data
S. I The database
5.2 Problem scenario
5.3 Setting up analysis services
5.4 Defining the OLAP cube
5.5 Adding to the dimensional representation
5.6 Building the analysis view for data mining
5.7 Setting up the data mining analysis
5.8 Predictive modeling (classification) tasks
5.9 Creating the mining model
5.10 The tree navigator
5.1 I Clustering (creating segments) with clusteranalysis
5.12 Confirming the model through validation
5.13 Summary
6 Deploying the Results
6.1 Deployments for predictive tasks (classification)
6.2 Lift charts
6.3 Backing up and restoring databases
7 The Discovery and Delivery of Knowledge for Effective
Enterprise Outcomes: Knowledge Management
7.1 The role of implicit and explicit knowledge
7.2 A primer on knowledge management
7.3 The Microsoft technology-enabling framework
7.4 Summary
Appendix A: Glossary
Appendix B: References
Appendix C: Web Sites
Appendix D: Data Mining and Knowledge Discovery
Data Sets in the Public Domain
Appendix E: Microsoft Solution Providers
Appendix F: Summary of Knowledge Management
Case Studies and Web Locations
Index.
Uniform Title:OLE (Computer file)
Uniform Title:SQL server.
Rubrics: Data mining

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