2001 Volume 13 Issue 4 Pages 406-417
Gaussian mixture model with EM algorithm is a popular density estimation method that uses the likelihood function as a measure of fit. It can be used as a tool for clustering. The thesis of the paper is that although the iterative algorithm of Fuzzy c-Means (FCM) clustering with entropy regularization is similar to that of the Gaussian mixture model, the FCM clustering has more flexible structure since the algorithm is based on the objective fanction method. We show that just the same algorithm as the Gaussian mixture model can be derived from a modified objective function with regularization by K-L information, and in a slightly different manner such as installing an annealing parameter and addition of Gustafson and Kessel's constraints, the proposed algorithm provides more valid or useful clustering results.
