Elsevier S & T

Microsoft Data Mining: Integrated Business Intelligence for e-Commerce and Knowledge Management

de Ville, Barry

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Table of contents
  • Cover
  • Copyright Pageiv
  • Contentsvii
  • Forewordxi
  • Prefacexiii
  • Acknowledgmentsxix
  • Chapter 1. Introduction to Data Mining1
  • 1.1 Something old, something new3
  • 1.2 Microsoft’s approach to developing the right set of tools7
  • 1.3 Benefits of data mining10
  • 1.4 Microsoft’s entry into data mining18
  • 1.5 Concept of operations19
  • Chapter 2. The Data Mining Process23
  • 2.1 Best practices in knowledge discovery in databases24
  • 2.2 The scientific method and the paradigms that come with it25
  • 2.3 How to develop your paradigm30
  • 2.4 The data mining process methodology37
  • 2.5 Business understanding39
  • 2.6 Data understanding41
  • 2.7 Data preparation44
  • 2.8 Modeling45
  • 2.9 Evaluation49
  • 2.10 Deployment51
  • 2.11 Performance measurement54
  • 2.12 Collaborative data mining: the confluence of data mining and knowledge management55
  • Chapter 3. Data Mining Tools and Techniques59
  • 3.1 Microsoft’s entry into data mining60
  • 3.2 The Microsoft data mining perspective60
  • 3.3 Data mining and exploration (DMX) projects64
  • 3.4 OLE DB for data mining architecture65
  • 3.5 The Microsoft data warehousing framework and alliance71
  • 3.6 Data mining tasks supported by SQL Server 2000 Analysis Services72
  • 3.7 Other elements of the Microsoft data mining strategy86
  • Chapter 4. Managing the Data Mining Project93
  • 4.1 The mining mart94
  • 4.2 Unit of analysis95
  • 4.3 Defining the level of aggregation97
  • 4.4 Defining metadata98
  • 4.5 Calculations99
  • 4.6 Standardized values102
  • 4.7 Transformations for discrete values103
  • 4.8 Aggregates103
  • 4.9 Enrichments111
  • 4.10 Example process (target marketing)112
  • 4.11 The data mart115
  • Chapter 5. Modeling Data117
  • 5.1 The database118
  • 5.2 Problem scenario118
  • 5.3 Setting up analysis services120
  • 5.4 Defining the OLAP cube124
  • 5.5 Adding to the dimensional representation132
  • 5.6 Building the analysis view for data mining135
  • 5.7 Setting up the data mining analysis137
  • 5.8 Predictive modeling (classification) tasks139
  • 5.9 Creating the mining model141
  • 5.10 The tree navigator147
  • 5.11 Clustering (creating segments) with cluster analysis151
  • 5.12 Confirming the model through validation158
  • 5.13 Summary159
  • Chapter 6. Deploying the Results163
  • 6.1 Deployments for predictive tasks (classification)164
  • 6.2 Lift charts172
  • 6.3 Backing up and restoring databases175
  • Chapter 7. The Discovery and Delivery of Knowledge for Effective Enterprise Outcomes: Knowledge Mana177
  • 7.1 The role of implicit and explicit knowledge179
  • 7.2 A primer on knowledge management180
  • 7.3 The Microsoft technology-enabling framework199
  • 7.4 Summary208
  • Appendix A: Glossary213
  • Appendix B: References219
  • Appendix C: Web Sites223
  • Appendix D: Data Mining and Knowledge Discovery Data Sets in the Public Domain229
  • Appendix E: Microsoft Solution Providers255
  • Appendix F: Summary of Knowledge Management Case Studies and Web Locations289
  • Index301
Book details
  • Vendor Elsevier S & T
  • SKU 9781555582425
  • ISBN-13 9780080491844
  • Author de Ville, Barry
  • Category Computers
  • Subject General

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Description eBook delivery & access Reviews
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