The basic aim of cluster analysis is to find the 'natural groupings', if any, of a set of individuals. Cluster analysis aims to allocate a set of individuals to a set of mutually exclusive, exhaustive, groups such that individuals within a group are similar to one another while individuals in different groups are dissimilar. This set of groups is usually called a partition. The groups forming a partition may be subdivided into smaller sets or grouped into larger sets, so that one eventually ends up with the complete hierarchical structure of the given set of individuals. This structure is often called a hierarchical tree. A variety of objectives have been suggested for cluster analysis. They include data exploration; data reduction; hypothesis generation; and prediction based on groups. This chapter considers three topics which are closely related to cluster analysis. The topics are clumping, dissection and the clustering of variables.