Cluster analysis is applied in many fields such as the natural sciences, the medical sciences, economics, marketing, etc. In marketing, for instance, it is useful to build and describe the different segments of a market from a survey on potential consumers. An insurance company, on the other hand, might be interested in the distinction among classes of potential customers so that it can derive optimal prices for its services. Other examples are provided below.
Discriminant analysis presented in Chapter 12 addresses the other issue of classification. It focuses on situations where the different groups are known a priori. Decision rules are provided in classifying a multivariate observation into one of the known groups.
Section 11.1 states the problem of cluster analysis where the criterion chosen to measure the similarity among objects clearly plays an important role. Section 11.2 shows how to precisely measure the proximity between objects. Finally, Section 11.3 provides some algorithms. We will concentrate on hierarchical algorithms only where the number of clusters is not known in advance.