Splitting-while-merging framework for clustering high-dimension data with component-wise expectation conditional maximisation

To meet the demand of clustering high dimensional data efficiently, in this paper, we propose a component-wise expectation conditional maximisation (CW-ECM) algorithm and integrate it within the recent proposed splitting-while-merging framework, which is called splitting-merging awareness tactics (SMART), for the mixture of factor analysers (MFA) model. The new algorithm has two advantages: it has ability to converge to actual or close actual number of clusters by a splitting-while-merging strategy, and it avoids the local maxima effectively and efficiently. Furthermore, we improve the splitting strategy in the original SMART framework and save more computational effort. We test out algorithm in two benchmark datasets and compare it with the state-of-the-art algorithms using many validation metrics. The results show that the proposed algorithm outperforms the compared algorithms in clustering performance with significantly less computational complexity.