Microsoft Data Mining Integrated Business Intelligence for e Commerce and Knowledge Management 1st Edition by Barry De Ville ISBN 1555582427 9781555582425
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Product details:
ISBN 10: 1555582427
ISBN 13: 9781555582425
Author: Barry De Ville
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
Microsoft Data Mining Integrated Business Intelligence for e Commerce and Knowledge Management 1st Table of contents:
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 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
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 strategy
Chapter 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
Chapter 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 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
5.11 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 knowledge
7.2 A primer on knowledge management
7.3 The Microsoft technology-enabling framework
7.4 Summary
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Tags: Barry De Ville, Microsoft Data, Business Intelligence, e Commerce