Processes | Pattern Discovery | K-Means Clustering

K-Means Clustering
K-Means clustering is a standard technique for partitioning data into groups with similar members.
The K-Means Clustering process creates optimally separated groups of observations (rows) of a SAS data set using one of several methods. A set of points called cluster seeds is selected as a first guess of the means of the clusters. Each observation is assigned to the nearest seed to form temporary clusters. The seeds are then replaced by the means of the temporary clusters, and the process is repeated until no further changes occur in the clusters.
You might wish to run Data Standardize before doing the clustering to ensure that the columns are all comparable.
What do I need?
One data set is required to run the K-Means Clustering process. This data set must be in a rectangular format and contain the variables (columns) whose observations (rows) are to be clustered.
The adsl_diit.sas7bdat data set, shown below, was generated from the included Nicardipine data set. Patients are listed in columns, and domain data are listed in rows. There are 911 columns for 906 patients and 318 rows listing events.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets.
The output generated by this process is summarized in a Tabbed report. Refer to the K-Means Clustering output documentation for detailed descriptions and guides to interpreting your results.