Applied Clustering Techniques

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

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	2 days
Live Web Classroom:	4 half-day session(s)	System Requirements

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.

This course addresses SAS/STAT software.

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