Cluster analysis

The aim of cluster analysis is categorization, i.e., the creation of groups of objects according to their similarities. The idea is hinted at in Fig. 15.3. There are other methods, such as discriminant analysis, essentially aimed at separating groups of observations. However, they differ in the underlying approach, and some can only deal with metric data. Cluster analysis relies on a distance measure; hence, provided we are able to define a distance with respect to qualitative attributes, it can cope with nonmetric variables. There is an array of cluster analysis methods, which are exploratory and aimed at studying interdependence; they are outlined in Section 17.4.