A Review of Classification

The summarization of large quantities of multivariate data by clusters, undefined a priori, is increasingly practiced, often irrelevantly and unjusti fiably. This paper attempts to survey the burgeoning bibliography, restrict ing itself to published, freely available, references of known provenance. A plethora of definitions of similarity and of cluster are presented. The principles, but not details of implementation, of the many empirical classi fication techniques currently in use are discussed, and limitations and short comings in their development and practice are pointed out. Methods based on well-defined mathematical formulations of the problem are emphasized, and other ways of summarizing data are suggested as alternatives to classi fication. The growing tendency to regard numerical taxonomy as a satis factory alternative to clear thinking is condemned.