Variable diagnostics in model-based clustering through variation partition

Model-based clustering is a flexible grouping technique based on fitting finite mixture models to data groups. Despite its rapid development in recent years, there is rather limited literature devoted to developing diagnostic tools for obtained clustering solutions. In this paper, a new method through fuzzy variation decomposition is proposed for probabilistic assessing contribution of variables to a detected dataset partition. Correlation between-variable contributions reveals the underlying variable interaction structure. A visualization tool illustrates whether two variables work collaboratively or exclusively in the model. Elimination of negative-effect variables in the partition leads to better classification results. The developed technique is employed on real-life datasets with promising results.