Microsoft Data Mining : Integrated Business Intelligence for e-Commerce and Knowledge Management
Barry. de Ville
Bok Engelsk 2001 · Electronic books.
| Utgitt | Burlington : : Elsevier Science, , 2001.
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| Omfang | 1 online resource (337 p.)
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| Opplysninger | Description based upon print version of record.. - Front Cover; Microsoft® Data Mining; Copyright Page; Contents; Foreword; Preface; Acknowledgments; Chapter 1. Introduction to Data Mining; 1.1 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; Chapter 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 understanding2.7 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; Chapter 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 alliance; 3.6 Data mining tasks supported by SQL Server 2000 Analysis Services. - 3.7 Other elements of the Microsoft data mining strategyChapter 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; Capter 5. Modeling Data; 5.1 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 mining5.7 Setting up the data mining analysis; 5.8 Predictive modeling (classification) tasks; 5.9 Creating the mining model; 5.10 The tree navigator; 5.11 Clustering (creating segments) with cluster analysis; 5.12 Confirming the model through validation; 5.13 Summary; Chapter 6. Deploying the Results; 6.1 Deployments for predictive tasks (classification); 6.2 Lift charts; 6.3 Backing up and restoring databases; Chapter 7. The Discovery and Delivery of Knowledge for Effective Enterprise Outcomes: Knowledge Management. - 7.1 The role of implicit and explicit knowledge7.2 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. - 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 t
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| ISBN | 1555582427
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