An enhanced splitting-while-merging algorithm with finite mixture models

In this paper, we propose a splitting-while-merging algorithm with finite mixture models (FMM) built on an improved splitting merging awareness tactics (SMART). The main property of SMART is that it does not require any dataset-dependent parameters or a priori knowledge about the datasets. The improved SMART framework integrates clustering selection criterion, which plays a vital role in the new algorithm. In the SMART-FMM implementation, the modified component-wise EM of mixtures is employed as a learning and merging technique and a model order selection algorithm is used as a clustering selection criterion. One demonstration example and one real microarray gene expression dataset are studied using our approach. The numerical results show that SMART-FMM is superior and more effective than others.