Cluster Analysis

This speech explains two classes of different kinds of clusters based on the algorithms included in the JMSL library of Visual Numerics for JAVA programmers.

Cluster explanations will be presented by using examples out of medical and astronomic (sort stars depending on luminosity and hotness) applications with the following classes:

ClusterKMeans performs a K-means cluster analysis.
Minimization within-cluster sums of squares. In this method of clustering the data, matrix X is grouped so that each observation (row in X) is assigned to one of a fixed number, K, of clusters. The sum of the squared difference of each observation about its assigned cluster’s mean is used as the criterion for assignment.

ClusterHierarchical conducts a hierarchical cluster analysis based upon a distance matrix, or by appropriate use of the argument transform, based upon a similarity matrix.
Class ClusterHierarchical conducts a hierarchical cluster analysis based upon a distance matrix, or by appropriate use of the argument transform, based upon a similarity matrix. Only the upper triangular part of the dist matrix is required as input.

Proceeding of hierarchical clustering with following contents:

• Find distance matrix
• Search in distance matrix two closest clusters
• Merge those two clusters to one cluster
• Set k = k + 1. If k is less than n, go to Step 2.