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.
Cluster analysis is widely used in the biological and behavioral sciences, ...
Get R in Action, Second Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
