Variational Mixture Models for Clustering Categorical Data
generateSampleDataBin
generateSampleDataCat
Minimize the posterior expected Variation of Information
runVICatMix
runVICatMixAvg
runVICatMixVarSel
runVICatMixVarSelAvg
Compute the modified Variation of Information from swapping log and ex...
A variational Bayesian finite mixture model for the clustering of categorical data, and can implement variable selection and semi-supervised outcome guiding if desired. Incorporates an option to perform model averaging over multiple initialisations to reduce the effects of local optima and improve the automatic estimation of the true number of clusters. For further details, see the paper by Rao and Kirk (2024) <doi:10.48550/arXiv.2406.16227>.