ON PARAMETER-ESTIMATION FOR NORMAL MIXTURES BASED ON FUZZY CLUSTERING ALGORITHMS

Described here are three approaches to estimate the parameters of a mixture of normal distributions. One approach is based on a modification of the expectation maximization algorithm to compute maximum likelihood estimates. Another makes use of the fuzzy c-means clustering algorithms, and the third is based on the penalized fuzzy c-means clustering algorithms. The accuracy and computational efficiency of algorithms of these three types to estimate parameters of normal mixtures are compared with samples drawn from univariate normal mixtures of two classes.