Data Mining Using Sas Applications by George Fernandez, Fernandez Fernandez

Product Details

Synopsis

After briefly reviewing basic concepts, this book describes statistical methods used for data mining and demonstrates how to perform business data mining tasks with SAS tools. Fernandez (University of Nevada) presents unsupervised learning methods for principal component analysis, exploratory factor analysis, and disjoint cluster analysis. He then discusses two supervised predictive model techniques based on multiple linear regression and binary logistic regression, and two supervised classification model techniques based on discriminant analysis and chi-squared automatic interaction detection. The 13 macros can be downloaded from a web site, and work with the standard SAS modules: BASE, STAT, GRAPH, and IML. Annotation (c)2003 Book News, Inc., Portland, OR

Annotation

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, downloadable SAS macro-call files. These techniques stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data. With the SAS macro-call files, readers will learn sampling techniques to create training and validation samples; exploratory graphical techniques, frequency analysis for categorical data, unsupervised and supervised learning methods; model validation techniques for regression and classification, and converting PC databases to SAS data.Experienced SAS programmers can also modify the SAS code to suit their needs and run it on different platforms. Sold separately, the CD-ROM contains datasets, macro call-files, and the actual SAS macro files.