Distribution-based clustering algorithms are based on statistical distribution models that provide more convenient ways to cluster related data objects to the same distribution. Although the theoretical foundations of these algorithms are very robust, they mostly suffer from overfitting. However, this limitation can be overcome by putting constraints on the model complexity.