A Dynamic Programming Algorithm for Cluster Analysis

TLDR: A dynamic programming approach is presented that reduces the amount of redundant transitional calculations implicit in a total enumeration approach to partitioning N entities into M disjoint and nonempty subsets clusters.

Abstract: This paper considers the problem of partitioning N entities into M disjoint and nonempty subsets clusters. Except when both N and N-M are very small, a search for the optimal solution by total enumeration of all clustering alternatives is quite impractical. The paper presents a dynamic programming approach that reduces the amount of redundant transitional calculations implicit in a total enumeration approach. A comparison of the number of calculations required under each approach is presented in Appendix A. Unlike most clustering approaches used in practice, the dynamic programming algorithm will always converge on the best clustering solution. The efficiency of the dynamic programming approach depends upon the rapid-access computer memory available. A numerical example is given in Appendix B.