Dual Criteria Determination of the Number of Clusters in Data

Kaixun Hua, Dan A. Simovici

Abstract: We present a method for determining the number of clusters existent in a data set involving a bi-criteria optimization that makes use of the entropy and the cohesion of a partition. The results are promising and may be applicable in dealing with clusterings of imbalanced data.