Probability density estimation using a Gaussian clustering algorithm

A version of the Traven's [1] Gaussian clustering algorithm for normal mixture densities is studied. Unlike in the case of the Tuaven's algorithm, no constraints on covariance structure of mixture components are imposed. Simulations suggest that the modified algorithm is a very promising method of estimating arbitrary continuous d-dimensional densities. In particular, the simulations have shown that the algorithm is robust against assuming the initial number of mixture components to be too large.