Applied Clustering Techniques - CLUS92
The course looks at the theoretical and practical implications of a wide array of clustering techniques currently available in SAS. The techniques considered include cluster preprocessing, variable clustering, k-nearest-neighbor clustering, k-means clustering, hierarchical clustering, and fuzzy clustering.
|2 days - Classroom|
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
- 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 Introduction to Statistics Using SAS®: ANOVA, Linear Regression and Logistic Regression course.
- have an understanding of matrix algebra.
Introduction to Clustering
- identifying types of clustering
- measuring similarity
- assessing multivariate normality
- using classification matrices
Preparation for Clustering
- using variable clustering for variable selection
- using graphical clustering aids
- making elongated clusters more spherical
- viewing the impact of input standardisation
- performing k-means clustering
- outlining the advantages of nonparametric clustering
- using hierarchical clustering methods
Assessing Clustering Results
- determining the number of clusters
- profiling a cluster solution
- scoring new observations
Canonical Discriminant Analysis (CDA) Plots
- introducing canonical discriminant analysis
- performing fuzzy clustering using the FACTOR procedure
- interpreting the PROC FACTOR output in terms of fuzzy clustering membership
This course addresses the following software product(s): SAS/STAT.