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

Includes index.

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.