I am attempting to cluster data using Mclust. The data is originally from a dissimilarity matrix, transformed via multidimensional scaling in R (
MASS::isoMDS). As I experiment with different numbers of dimensions, I have found that with larger numbers of dimensions (~50 relative to ~10 for the smaller values tried) certain model types return
NA values for the BIC with larger numbers of components, i.e. clusters.
The Mclust manual states
The missing values correspond to models and numbers of clusters for which parameter values could not be fit (using the default initialization). For multivariate data, the default initialization for all models uses the classification from hierarchical clustering based on an unconstrained model.
Can anyone explain what causes the failure of the parameter values to fit, and how that relates to the number of dimensions being used? Knowing this might help me better understand the potential value or lack thereof of those later dimensions.
In case it is relevant, in the cases I have tried the model type VVV stops at a lower number of components followed by EVI and VEV, then by VEI and VII, with the other 4 (EEI, EII, EEE, and EEV) returning values up to the default max of 9 components. I can run any number of other numbers of dimensions to derive a clearer pattern if that is helpful.