A Modified Fuzzy K-means Clustering using Expectation Maximization

K-means is a popular clustering algorithm that requires a huge initial set to start the clustering. K-means is an unsupervised clustering method which does not guarantee convergence. Numerous improvements to K-means have been done to make its performance better. Expectation Maximization is a statistical technique for maximum likelihood estimation using mixture models. It searches for a local maxima and generally converges very well. The proposed algorithm combines these two algorithms to generate optimum clusters which do not require a huge value of K and each cluster attains a more natural shape and guarantee convergence. The paper compares the new method with Fuzzy K-means on benchmark iris data.