Model-based clustering and LVMM require a choice of a multivariate distribution for the observed symptoms within each cluster. Single class models correspond to dimensionality, and multiple class models indicate taxonicity. The key assumption in both methods is that the component distributions correspond to meaningful clusters. The methods permit modeling continuous data and also binary (yes/no) or ordinal observed data (Likert scales). In model-based clustering the user can compare models that allow differences between clusters regarding shape, volume and orientation, or constrain any of the related parameters to be equal across clusters. Some of the possible parameterizations result in models that are equivalent with certain LVMMs (i.e. latent class models).
