Modeling and Multivariate Methods - SAS

470 Clustering Data Chapter 18
K-Means Clustering
K-Means Control Panel
As an example of KMeans clustering, use the Cytometry.jmp sample data table. Add the variables CD3 and
CD8 as Y, Columns variables. Select the KMeans option. Click OK. The Control Panel appears, and is
shown in Figure 18.5.
Figure 18.5 Iterative Clustering Control Panel
The Iterative Clustering red-triangle menu has the Save Transformed option. This saves the Johnson
transformed variables to the data table. This option is available only if the Johnson Transform option is
selected on the launch dialog (Figure 18.4).
The Control Panel has these options:
Declutter is used to locate outliers in the multivariate sense. Plots are produced giving distances between
each point and that points nearest neighbor, the second nearest neighbor, up to the k th nearest neighbor.
You are prompted to enter k. Beneath the plots are options to create a scatterplot matrix, save the
distances to the data table, or to not include rows that you have excluded in the clustering procedure. If
an outlier is identified, you might want to exclude the row from the clustering process.
Method
is used to choose the clustering method. The available methods are:
KMeans Clustering is described in this section.
Normal Mixtures is described in “Normal Mixtures” on page 473.
Robust Normal Mixtures is described in “Normal Mixtures” on page 473.
Self Organizing Map is described in “Self Organizing Maps” on page 477.
Number of Clusters
is the number of clusters to form.
Optional range of clusters is an upper bound for the number of clusters to form. If a number is entered
here, the platform creates separate analyses for every integer between Number of clusters and this one.