Edited by author 10-30-2001 01:08 AM
I wish to know if the dissimilarity measures mentioned in the paper can be used to improve the performance of existing clustering algorithms like the EM algorithm by regularization. It is well known that the EM tries to maximize the likelihood function. We can modify the likelihood to obtain a penalized likelihood where the penalizing term is a function of the dissimilarity measure between clusters. By maximizing this modified objective function, we can obtain clustering algorithms that provide better segmentation.

Any thoughts on this ?