The course looks at the theoretical and practical implications of a wide array of clustering techniques that are currently available in SAS. The techniques considered include cluster preprocessing, variable clustering, k-means clustering, and hierarchical clustering.
Learn How To
Prepare and explore data for a cluster analysis.
Distinguish among many different clustering techniques, making informed choices about which to use.
Evaluate the results of a cluster analysis.
Determine the appropriate number of clusters to retain.
Profile and describe clustered observations.
Score observations into clusters.
Who Should Attend
Intermediate- or senior-level statisticians, data analysts, and data miners
Before attending this course, you should:;
Be able to execute SAS programs and create SAS data sets. You can gain this experience by completing the SAS Programming 1: Essentials course.
Have completed a graduate-level course in statistics or the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.