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.

Have an understanding of matrix algebra.

Introduction to Clustering

Overview.

Types of clustering in this course.

Preparation for Clustering

Sample selection.

Variable selection.

Variable standardization.

Graphical aids to clustering.

Within cluster variable transformation.

Hierarchical Clustering

Measuring similarity.

Hierarchical clustering methods.

Determining the number of clusters.

k-Means Clustering

The k-means clustering algorithm.

k-means clustering using the FASTCLUS procedure.

Determining the number of clusters.

Nonparametric Clustering

Nonparametric clustering.

Practices.

Cluster Profiling and Scoring

Cluster profiling.

Scoring new observations.

Appendix A: Canonical Discriminant Analysis (CDA) Plots Appendix B: Fuzzy Clustering Appendix C: Assessing Multivariate Normality Appendix D: References