A new clustering evaluation function using Renyi's information potential
Clustering is an important unsupervised learning paradigm, but so far the traditional methodologies are mostly based on the minimization of the variance between the data and the cluster means. Here we propose a new evaluation function based on a previously developed information theoretic measure defined from Renyi's (1960) entropy. We show how to apply Renyi's entropy to clustering and analyze the resulting staircase nature of the performance function that can be expected during learning. We suggest simulated annealing as a possible optimization criterion.