Statistics Toolbox

Cluster Analysis

Cluster analysis, also called segmentation analysis or taxonomy analysis, is a way to way to create groups of objects, or clusters, in such a way that the profiles of objects in the same cluster are very similar and the profiles of objects in different clusters are quite distinct.

Cluster analysis can be performed on many different types of data sets. For example, a data set might contain a number of observations of subjects in a study where each observation contains a set of variables.

Many different fields of study, such as engineering, zoology, medicine, linguistics, anthropology, psychology, and marketing, have contributed to the development of clustering techniques and the application of such techniques. For example, cluster analysis can help in creating "balanced" treatment and control groups for a designed study. If you find that each cluster contains roughly equal numbers of treatment and control subjects, then statistical differences found between the groups can be attributed to the experiment and not to any initial difference between the groups.

This sections explores two kinds of clustering:

• Hierarchical Clustering
• K-Means Clustering

Multivariate Analysis of Variance (MANOVA)

Hierarchical Clustering