Chapter 16. Cluster analysis
This chapter covers
- Identifying cohesive subgroups (clusters) of observations
- Determining the number of clusters present
- Obtaining a nested hierarchy of clusters
- Obtaining discrete clusters
Cluster analysis is a data-reduction technique designed to uncover subgroups of observations within a dataset. It allows you to reduce a large number of observations to a much smaller number of clusters or types. A cluster is defined as a group of observations that are more similar to each other than they are to the observations in other groups. This isn’t a precise definition, and that fact has led to an enormous variety of clustering methods.