Capabilities

The FASTCLUS Procedure

performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables
observations are divided into clusters such that every observation belongs to one and only one cluster
uses Euclidean distances, so the cluster centers are based on least squares estimation (k-means 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
obtain separate analysis on observations in groups
compute weighted cluster means
uses ODS to create a SAS data set corresponding to any table

For further details see the SAS/STAT User's Guide: The FASTCLUS Procedure
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