• Covers fundamental concepts before moving on to practical applications
• Provides user-friendly SAS macro-call files through a supporting Web site at http://www.ag.unr.edu/gf/dm.html which also contains tips, updates, datasets, and an e-mail support service
• Contains step-by-step instructions for performing data mining on sample datasets
• Includes guidelines for performing complete data analysis, including sampling, data exploration, violation checking, model validations, and options for report generationSold separately, a companion CD-ROM contains all of the datasets, macro call-files, and the actual SAS macro files used in the book. See ISBN number 1-58488-379-0, Data Mining Using SAS Applications CD-ROM.

Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data.

• Learn how to convert PC databases to SAS data
• Discover sampling techniques to create training and validation samples
• Understand frequency data analysis for categorical data
• Explore supervised and unsupervised learning
• Master exploratory graphical techniques
• Acquire model validation techniques in regression and classification

The text furnishes 13 easy-to-use SAS data mining macros designed to work with the standard SAS modules. No additional modules or previous experience in SAS programming is required. The author shows how to perform complete predictive modeling, including data exploration, model fitting, assumption checks, validation, and scoring new data, on SAS datasets in less than ten minutes!

Contents

• Data Mining - A Gentle Introduction
• Preparing Data for Data Mining
• Exploratory Data analysis
• Unsupervised Learning Methods
• Supervised Learning Methods - Prediction
• Supervised Learning Methods -Classification
• Emerging Technologies in Data Mining
• Appendix: Instruction for Using the SAS Macros