Synopses & Reviews
The amount of information stored in corporate databases is exploding exponentially. Data mining—finding meaningful patterns in all that data—can give any organization a competitive advantage. This book is the in-depth reference from Microsoft for anyone who wants to take full advantage of the powerful data-mining features in SQL Server 2000. It examines the SQL Server 2000 Analysis Services architecture and shows how data mining fits into its complete suite of information-extraction technologies. Then it demonstrates how to structure and mine large databases with the algorithms included with SQL Server 2000 to find nuggets of useful information. It even shows how to create a practice data-mining model using data downloaded from a database. Coverage includes:
- INTRODUCTION TO DATA MINING: What data mining is and isn’t, plus important principles and definitions behind data-mining methodologies, including the role of data-mining models, statistics, and algorithms
- SQL SERVER 2000 ARCHITECTURE: How data mining fits into the SQL Server 2000 Analysis Services architecture and how it builds on the SQL Server 2000 relational database and its embedded online analytical processing (OLAP) engine
- DATA-MINING METHODS: How to choose the best data-mining method for the job—decision trees or clustering
- EASE OF USE FEATURES: How to use the Mining Model Wizard and the OLAP Mining Model Editor to simplify creating, training, and processing a model
- PROGRAMMING THE DATA-MINING SERVICES: How to use data-mining models and Data Transformation Services, PivotTable Services, decision-support objects (DSO), PERL, Visual Basic, Scripting Edition, XML, and other tools and languages to work with the data-mining engine
This guide to uncovering hidden information and meaningful patterns in large databases uses two sample databases to illustrate how to build a data-mining model. The author explains the SQL server analysis services architecture, data storage methods, how to create decision trees with online analytica
This technical reference is the ideal, in-depth guide for any database developer, administrator, or IT professional who needs comprehensive information about these powerful new data-mining services. It fully examines the data-warehousing architecture in SQL Server 2000 to show how to take full advantage of the data-mining services in this RDBMS. (Computer Books - Database Management)
With its state-of-the-art capabilities for rapidly processing and retrieving huge quantities of data, Microsoft SQL Server 2000 is quickly growing in popularity among large corporations. But learning how to take advantage of the powerful, built-in data-mining services in SQL Server to turn all that data into meaningful information takes time and effort. Data Mining with SQL Server 2000 Technical Reference is the ideal, in-depth reference guide for any database developer, administrator, or IT professional who needs comprehensive information about these powerful new data-mining services. In particular, it fully examines the data-warehousing architecture in SQL Server 2000 to show how to take full advantage of the data-mining services in this RDBMS. This is the only Microsoft-approved technical guide to the data mining services in SQL Server 2000.
About the Author
Claude Seidman has been a software developer, DBA, and trainer since 1987 and has been using SQL Server since version 4.2. He specializes in SQL Server design and development as well as building decision support systems with Microsoft OLAP for use on the web. Claude has written articles in several publications, including SQL Server Magazine. He holds the MCSE, MCSD, MCDBA, MCP+I, and MCT certifications from Microsoft Magazine. Besides developing applications and administering databases, Claude teaches the MCSE track at a local university.
Table of Contents
Acknowledgments; Introduction; Who Should Use This Book; What Is in This Book; Introducing Data Mining; Chapter 1: Understanding Data Mining; 1.1 What Is Data Mining?; 1.2 Why Use Data Mining?; 1.3 How Data Mining Is Currently Used; 1.4 Defining the Terms; 1.5 Data Mining Methodology; 1.6 Overview of Microsoft Data Mining; 1.7 Summary; Chapter 2: Microsoft SQL Server Analysis Services Architecture; 2.1 Introduction to OLAP; 2.2 Server Architecture; 2.3 Client Architecture; 2.4 Summary; Chapter 3: Data Storage Models; 3.1 Why Data Mining Needs a Data Warehouse; 3.2 Reporting Against OLTP Data Can Be Hazardous to Your Performance; 3.3 Data Warehousing Architecture for Data Mining; 3.4 Relational Data Warehouse; 3.5 OLAP cubes; 3.6 Summary; Chapter 4: Approaches to Data Mining; 4.1 Directed Data Mining; 4.2 Undirected Data Mining; 4.3 Training Data-Mining Models; 4.4 Summary; Data-Mining Methods; Chapter 5: Microsoft Decision Trees; 5.1 Creating the Model; 5.2 Visualizing the Model; 5.3 How Predictions Are Derived; 5.4 Summary; Chapter 6: Creating Decision Trees with OLAP; 6.1 Creating the Model; 6.2 OLAP Mining Model Editor; 6.3 Analyzing Data with the OLAP Data-Mining Model; 6.4 Summary; Chapter 7: Microsoft Clustering; 7.1 The Search for Order; 7.2 Looking for Ways to Understand Data; 7.3 Clustering as an Undirected Data-Mining Technique; 7.4 How Clustering Works; 7.5 When to Use Clustering; 7.6 Creating a Data-Mining Model Using Clustering; 7.7 Viewing the Model; 7.8 Analyzing the Data; 7.9 Summary; Creating Data-Mining Applications with Code; Chapter 8: Using Microsoft Data Transformation Services (DTS); 8.1 What Is DTS?; 8.2 DTS Tasks; 8.3 Connections; 8.4 DTS Package Workflow; 8.5 DTS Designer; 8.6 dtsrun Utility; 8.7 Using DTS to Create a Data-Mining Model; 8.8 Summary; Chapter 9: Using Decision Support Objects (DSO); 9.1 Scripting vs. Visual Basic; 9.2 Creating the Relational Data-Mining Model Using DSO; 9.3 Creating the OLAP Data-Mining Model Using DSO; 9.4 Adding a New Data Source; 9.5 Analysis Server Roles; 9.6 Summary; Chapter 10: Understanding Data-Mining Structures; 10.1 The Structure of the Data-Mining Model Case; 10.2 Using Code to Browse Data-Mining Models; 10.3 Using the Schema Rowsets; 10.4 Summary; Chapter 11: Data Mining Using PivotTable Service; 11.1 Redistributing Components; 11.2 Installing and Registering Components; 11.3 Connecting to the PivotTable Service; 11.4 Building a Local Data-Mining Model; 11.5 Using XML in Data Mining; 11.6 Summary; Chapter 12: Data-Mining Queries; 12.1 Components of a Prediction Query; 12.2 Prediction Queries with Clustering Models; 12.3 Using DTS to Run Prediction Queries; 12.4 Summary; Regression Analysis; What Is Regression Analysis?; Predicting Continuous Attributes: An Example; The Regression Line; Using Regression Analysis to Make Predictions; Analyzing the Accuracy of the Regression Line; Using OLAP to Create Regression Models; Applying Regression to a Relational Database; Using Visual Basic to Perform Regression Analysis; Creating the Models; Summary; Glossary of Data-Mining Terms; About the Author;