Fuzzy C-Means Clustering With Regularization by K-L Information

Gaussian mixture model or Gaussian mixture density model(GMM) uses the likelihood function as a measure of fit. We show that just the same algorithm as the GMM can be derived from a modified objective function of Fuzzy c-Means (FCM) clustering with the regularizer by K-L information, only when the parameter λ equals 2. Although the fixed-point iteration scheme of FCM is similar to that of the GMM, the FCM has more flexible structure since the algorithm is based on the objective function method. In a slightly different manner such as installing a deterministic annealing or an addition of Gustafson and Kessel’s constraint, the proposed algorithm is likely to provide more valid clustering results.