Use Cluster K-Means to group observations into clusters that share common characteristics. This method is appropriate when you have sufficient information to make good starting cluster designations for the clusters.
For example, a business analyst uses cluster K-means to classify 22 successful small-to-medium size manufacturing companies into meaningful groups for future analyses. To start the partition process, the analyst divides the companies into three initial groups: established, mid-growth, and young.
Cluster K-means uses a non-hierarchical procedure to group observations. Therefore, in the clustering process, two observations might be split into separate clusters after they are joined together.
To perform a cluster K-means analysis, choose.