This book is an invaluable companion to SQL Server 2005 Data Mining. The authors explain the basic principles of each algorithm and visualization tool, and provide many hands-on examples. I am certain that many database developers, database administrators, IT professionals, and students of data mining will benefit from this book.
Data mining is about analyzing data and finding hidden patterns using automatic or semiautomatic means. During the past decade, large volumes of data have been accumulated and stored in databases. Much of this data comes from business software, such as financial applications, Enterprise Resource Management (ERP), Customer Relationship Management (CRM), and Web logs. The result of this data collection is that organizations have become data-rich and knowledge-poor. The collections of data have become so vast and are increasing so rapidly in size that the practical use of these stores of data has become limited. The main purpose of data mining is to extract patterns from the data at hand, increase its intrinsic value and transfer the data to knowledge.
Concealed inside your data warehouse and data marts is a wealth of valuable information just waiting to be discovered. All you need are the right tools to extract that information and put it to use. Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets. The authors explore the core concepts of data mining as well as the latest trends. They then reveal the best practices in the field, utilizing the innovative features of SQL Server 2005 so that you can begin building your own successful data mining projects.
TABLE OF CONTENT:
Chapter 01 - Introduction to Data Mining
Chapter 02 - OLE DB for Data Mining
Chapter 03 - Using SQL Server Data Mining
Chapter 04 - Microsoft Naive Bayes
Chapter 05 - Microsoft Decision Trees
Chapter 06 - Microsoft Time Series
Chapter 07 - Microsoft Clustering
Chapter 08 - Microsoft Sequence Clustering
Chapter 09 - Microsoft Association Rules
Chapter 10 - Microsoft Neural Network
Chapter 11 - Mining OLAP Cubes
Chapter 12 - Data Mining with SQL Server Integration Services
Chapter 13 - SQL Server Data Mining Architecture
Chapter 14 - Programming SQL Server Data Mining
Chapter 15 - Implementing a Web Cross-Selling Application
Chapter 16 - Advanced Forecasting Using Microsoft Excel
Chapter 17 - Extending SQL Server Data Mining
Chapter 18 - Conclusion and Additional Resources
Appendix A - Importing Datasets
Appendix B - Supported VBA and Excel Functions
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