I'm looking to cluster a small data set (64 observations of 4 interval variables and a single three-factor categorical variable). Now, I'm quite new to cluster analysis, but I am aware that there has been considerable progress since the days when hierarchical clustering or k-means were the only available options. In particular, it seems that new methods of model based clustering are available that, as pointed out by chl, enable the use of "goodness-of-fit indices to decide about the number of clusters or classes".
However, the standard R package for model based clustering
mclust apparently will not fit models with mixed data types. The
fpc model will, but has trouble fitting a model, I suspect because of the non-gaussian nature of the continuous variables. Should I continue with the model-based approach? I'd like to continue to use R if possible. As I see it, I have a few options:
- Convert the three-level categorical variable into two dummy variables and use
mclust. I'm unsure if this will bias the results, but if not this is my preferred option.
- Transform the continuous variables somehow and use the
- Use some other R package I haven't yet encountered.
- Create a dissimilarity matrix using Gower's measure and use traditional hierarchical or relocation cluster techniques.
Does the stats.se hivemind have any suggestions here?