Parsimonious Gaussian mixture models of general family for binned data clustering: Mixture approach

Binning data provides a solution in deducing computation expense in cluster analysis. According to former study, basing cluster analysis on Gaussian mixture models has become a classical and power approach. Mixture approach is one of the most common model-based approaches, which estimates the model parameters by maximizing the likelihood by EM algorithm. According to eigenvalue composition of the variance matrices of the mixture components, parsimonious models are generated. Choosing a right parsimonious model is crucial in obtaining a good result. In this paper, we address the problem of applying mixture approach to binned data (binned-EM algorithm). Six general models are studied and the difference in the performances of six general models is analyzed.