Modeling and Multivariate Methods - SAS

462 Clustering Data Chapter 18
The Cluster Launch Dialog
• For each cluster, a new center is formed using every observation with its probability of membership as a
weight. This is the maximization step.
This process continues alternating between the expectation and maximization steps until the clusters
become stable.
Note: For k-means clustering, you can choose a variable whose values form preset fixed centers for clusters,
instead of using the default random seeds for clusters.
The Cluster Launch Dialog
When you choose Cluster from the Analyze > Multivariate Methods submenu, the Hierarchical Cluster
Launch dialog shown in Figure 18.1 appears. The data table used is Birth Death Subset.jmp.
Choose KMeans from the Options menu to see the KMeans launch dialog. See “K-Means Clustering” on
page 469 for more information about the KMeans method.
Figure 18.1 Hierarchical Cluster Launch Dialog
You can specify as many Y variables as you want by selecting the variables in the column selector list and
clicking Y, Columns.
For Hierarchical clustering, select Hierarchical from the Options list and then select one of the clustering
distance options: Average, Centroid, Ward, Single, and Complete, and Fast Ward. The clustering methods
differ in how the distance between two clusters is computed. These clustering methods are discussed under
“Technical Details for Hierarchical Clustering” on page 467.
By default, data are first standardized by the column mean and standard deviation. Uncheck the
Standardize Data check box if you do not want the cluster distances computed on standardized values.