Maximum likelihood clustering via normal mixture models

摘要

We present the approach to clustering whereby a normal mixture model is fitted to the data by maximum likelihood. The general case of normal component densities with unrestricted covariance matrices is considered and so it extends the work of, who imposed the rest, who imposed the restriction of diagonal component covariance matrices. Attention is also focussed on the problem of testing for the number of clusters within this mixture framework, using the likelihood ratio test.