There is a new version of this course. Please see Applied Clustering Techniques.

Formats available	Standard Duration
Classroom:	2.0 days

This course addresses SAS/STAT software.

• identifying types of clustering
• measuring similarity
• assessing multivariate normality
• using classification matrices

• using variable clustering for variable selection
• using graphical clustering aids
• making elongated clusters more spherical
• viewing the impact of input standardization

• k-means clustering for segmentation
• outlining the advantages of nonparametric clustering

• comparing hierarchical clustering methods

• determining the number of clusters
• profiling a cluster solution
• scoring new observations

• variable selection
• graphical exploration of selected variables
• hierarchical clustering and determining the number of clusters
• profiling the seven-cluster solution
• modeling cluster membership
• scoring the customer database

• using canonical discriminant analysis to summarize multivariate data
• interpreting CANDISC procedure output

• performing fuzzy clustering using the FACTOR procedure
• interpreting the PROC FACTOR output in terms of fuzzy clustering membership