Current Methods of Cluster Analysis: An Overview

Abstract

For the most part, statisticians have been concerned with uncovering the interrelationships that exist among variables. The objects upon which the measurements were taken were assumed to be homogeneous. Rarely was interest in the possibility that a given set of objects could be grouped into subsets that displayed systematic differences. In many applications, however, there is reason to believe that a set of objects can be clustered into subgroups that differ in meaningful ways. In large data sets like those stored in clinical cancer registers, nobody would expect the objects to be homogeneous. The most commonly used term for the class of procedures that seek to separate the component data into groups is cluster analysis. Other names which are used synonymously are automatic classification, numerical classification, numerical taxonomy, and sometimes pattern recognition.