Modeling and Multivariate Methods - SAS

Chapter 18 Clustering Data 471
K-Means Clustering
Single Step enables you to step through the clustering process one iteration at a time using a Step
button, or automate the process using a Go button.
Use within-cluster std deviations If you do not use this option, all distances are scaled by an overall
estimate of the standard deviation of each variable. If you use this option, distances are scaled by the
standard deviation estimated for each cluster.
Shift distances using sampling rates assumes that you have a mix of unequally sized clusters, and
points should give preference to being assigned to larger clusters because there is a greater prior
probability that it is from a larger cluster. This option is an advanced feature. The calculations for this
option are implied, but not shown for normal mixtures.
K-Means Report
Clicking Go in the Control Panel in Figure 18.5 produces the K-Means report, shown in Figure 18.6.
Figure 18.6 K-Means Report
The report gives summary statistics for each cluster:
• count of number of observations
• means for each variable
• standard deviations for each variable.
The Cluster Comparison report gives fit statistics to compare different numbers of clusters. For KMeans
Clustering and Self Organizing Maps, the fit statistic is CCC (Cubic Clustering Criterion). For Normal
Mixtures, the fit statistic is BIC or AICc. Robust Normal Mixtures does not provide a fit statistic.