Book description
Understand how to use the new features of Microsoft SQL Server 2008 for data mining by using the tools in Data Mining with Microsoft SQL Server 2008, which will show you how to use the SQL Server Data Mining Toolset with Office 2007 to mine and analyze data. Explore each of the major data mining algorithms, including naive bayes, decision trees, time series, clustering, association rules, and neural networks. Learn more about topics like mining OLAP databases, data mining with SQL Server Integration Services 2008, and using Microsoft data mining to solve business analysis problems.
Table of contents
- Copyright
- About the Authors
- Credits
- Acknowledgments
- Foreword
- Introduction
- 1. Introduction to Data Mining in SQL Server 2008
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2. Applied Data Mining Using Microsoft Excel 2007
- 2.1. Setting Up the Table Analysis Tools
- 2.2. The Analyze Key Influencers Tool
- 2.3. The Detect Categories Tool
- 2.4. The Fill From Example Tool
- 2.5. The Forecasting Tool
- 2.6. The Highlight Exceptions Tool
- 2.7. The Scenario Analysis Tool
- 2.8. The Prediction Calculator Tool
- 2.9. The Shopping Basket Analysis Tool
- 2.10. Technical Overview of the Table Analysis Tools
- 2.11. Summary
-
3. Data Mining Concepts and DMX
- 3.1. History of DMX
- 3.2. Why DMX?
- 3.3. The Data Mining Process
- 3.4. Key Concepts
- 3.5. DMX Objects
- 3.6. DMX Query Syntax
- 3.7. Prediction
- 3.8. Summary
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4. Using SQL Server Data Mining
- 4.1. Introducing the Business Intelligence Development Studio
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4.2. Setting Up Your Data Sources
- 4.2.1. Understanding Data Sources
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4.2.2. Using the Data Source View
- 4.2.2.1. Creating the MovieClick Data Source View
- 4.2.2.2. Working with Named Calculations
- 4.2.2.3. Creating a Named Calculation on the Customers Table
- 4.2.2.4. Working with Named Queries
- 4.2.2.5. Creating a Named Query Based on the Customers Table
- 4.2.2.6. Organizing the DSV
- 4.2.2.7. Exploring Data
- 4.3. Creating and Editing Models
- 4.4. Processing
- 4.5. Using Your Models
- 4.6. Using SQL Server Management Studio
- 4.7. Summary
- 5. Implementing a Data Mining Process Using Office 2007
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6. Microsoft Naïve Bayes
- 6.1. Introducing the Naïve Bayes Algorithm
- 6.2. Using the Naïve Bayes Algorithm
- 6.3. Understanding Naïve Bayes Principles
- 6.4. Naïve Bayes Parameters
- 6.5. Summary
- 7. Microsoft Decision Trees Algorithm
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8. Microsoft Time Series Algorithm
- 8.1. Overview
- 8.2. Usage
- 8.3. DMX
- 8.4. Principles of Time Series
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8.5. Parameters
- 8.5.1. MISSING_VALUE_SUBSTITUTION
- 8.5.2. PERIODICITY_HINT
- 8.5.3. AUTO_DETECT_PERIODICITY
- 8.5.4. MINIMUM and MAXIMUM_SERIES_VALUE
- 8.5.5. FORECAST_METHOD
- 8.5.6. PREDICTION_SMOOTHING
- 8.5.7. INSTABILITY_SENSITIVITY
- 8.5.8. HISTORIC_MODEL_COUNT and HISTORIC_MODEL_GAP
- 8.5.9. COMPLEXITY_PENALTY and MINIMUM_SUPPORT
- 8.6. Model Content
- 8.7. Summary
- 9. Microsoft Clustering
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10. Microsoft Sequence Clustering
- 10.1. Introducing the Microsoft Sequence Clustering Algorithm
- 10.2. Using the Microsoft Sequence Clustering Algorithm
- 10.3. Microsoft Sequence Clustering Algorithm Principles
- 10.4. Model Content
- 10.5. Algorithm Parameters
- 10.6. Summary
- 11. Microsoft Association Rules
- 12. Microsoft Neural Network and Logistic Regression
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13. Mining OLAP Cubes
- 13.1. Introducing OLAP
- 13.2. Performing Calculations
- 13.3. Browsing a Cube
- 13.4. Understanding Unified Dimension Modeling
- 13.5. Understanding the Relationship between OLAP and Data Mining
- 13.6. Building OLAP Mining Models Using Wizards and Editors
- 13.7. Understanding Data Mining Dimensions
- 13.8. Using MDX within DMX Queries
- 13.9. Using Analysis Management Objects for the OLAP Mining Model
- 13.10. Summary
- 14. Data Mining with SQL Server Integration Services
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15. SQL Server Data Mining Architecture
- 15.1. Introducing Analysis Services Architecture
- 15.2. XML for Analysis
- 15.3. Processing Architecture
- 15.4. Predictions
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15.5. Data Mining Administration
- 15.5.1. Server Configuration
- 15.5.2. Data Mining Security
- 15.5.3. Security Requirements for Creating and Training Mining Objects
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15.5.4. Security for Various Deployment Scenarios
- 15.5.4.1. Local Database and Analysis Services
- 15.5.4.2. Local Analysis Services and a Remote Database
- 15.5.4.3. Intranet Analysis Services and Databases on the Same Server
- 15.5.4.4. Analysis Services and Databases behind an HTTP Endpoint in an Internet Deployment
- 15.5.4.5. Configuring Analysis Services for Use with Data Mining Excel Add-Ins over HTTP
- 15.6. Summary
- 16. Programming SQL Server Data Mining
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17. Extending SQL Server Data Mining
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17.1. Plug-in Algorithms
- 17.1.1. Plug-in Algorithm Framework
- 17.1.2. Lifetime of a Plug-in Algorithm Instance
- 17.1.3. Conceptual Overview
- 17.1.4. Model Creation and Processing
- 17.1.5. Prediction
- 17.1.6. Content Navigation
- 17.1.7. Custom Functions
- 17.1.8. PMML
- 17.1.9. Managed vs. Native Plug-ins
- 17.1.10. Installing Plug-in Algorithms
- 17.1.11. Where to Find Out More about Plug-in Algorithms
- 17.2. Data Mining Viewers
- 17.3. Summary
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17.1. Plug-in Algorithms
- 18. Implementing a Web Cross-Selling Application
- 19. Conclusion and Additional Resources
- A. Data Sets
- B. Supported Functions
Product information
- Title: Data Mining with Microsoft® SQL Server® 2008
- Author(s):
- Release date: November 2008
- Publisher(s): Wiley
- ISBN: 9780470277744
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