Multivariate normal mixture modeling, clustering and classification with the rebmix package
This is an incremental contribution providing a software package for statistical modeling and analysis in domains like data science and machine learning.
The paper tackles the problem of generating, estimating, clustering, and classifying multivariate normal mixture models with unrestricted variance-covariance matrices using the rebmix package in R, demonstrating its functionality on a dataset.
The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted variance-covariance matrices. The objective is to show how to generate datasets for a known number of components, numbers of observations and component parameters, how to estimate the number of components, component weights and component parameters and how to predict cluster and class membership based upon a model trained by the REBMIX algorithm. The accompanying plotting, bootstrapping and other features of the package are dealt with, too. For demonstration purpose a multivariate normal dataset with unrestricted variance-covariance matrices is studied.