Use the K Means Cluster platform to group observations that share similar values across a number of variables. Use the k-means method with larger data tables, ranging from approximately 200 to 100,000 observations.
The K Means Cluster platform constructs a specified number of clusters using an iterative algorithm that partitions the observations. The method, called k-means, partitions observations into clusters so as to minimize distances to cluster centroids. You must specify the number of clusters, k, in advance. However, you can compare the results of different values of k to select an optimal number of clusters for your data.
Figure 11.1 3D Biplot
