FASTCLUS Procedure
The FASTCLUS procedure performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
The observations are divided into clusters such that every observation belongs to one and only one cluster.
The following are highlights of the procedure's features:
 uses Euclidean distances, so the cluster centers are based on least squares estimation (kmeans model)
 designed to find good clusters (but not necessarily the best possible clusters) with only two or three
passes through the data set
 can be an effective procedure for detecting outliers because outliers often appear as clusters with only one member
 can use an L_{p} (least pth powers) clustering criterion
 is intended for use with large data sets, with 100 or more observations

 uses algorithms that place a larger influence on variables with larger variance
 produces brief summaries of the clusters
 produces an output data set containing a cluster membership variable
 performs BY group processing, which enables you to obtain separate analysis on grouped observations
 computes weighted cluster means
 creates a SAS data set that corresponds to any output table

For further details see the FASTCLUS Procedure
Examples