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
Prerequisites
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.SAS Products Covered
SAS/STAT
Course Outline
Introduction to Clustering
Overview.Types of clustering in this course.Preparation for ClusteringSample selection.Variable selection.Variable standardization. Graphical aids to clustering.Within cluster variable transformation.Hierarchical ClusteringMeasuring similarity.Hierarchical clustering methods.Determining the number of clusters.k-Means ClusteringThe k-means clustering algorithm.k-means clustering using the FASTCLUS procedure.Determining the number of clusters.Nonparametric ClusteringNonparametric clustering.Practices.Cluster Profiling and ScoringCluster profiling.Scoring new observations.Appendix A: Canonical Discriminant Analysis (CDA) Plots Appendix B: Fuzzy Clustering Appendix C: Assessing Multivariate Normality Appendix D: References
Live Instructor Dates