CoCE-SMART: Consensus clustering based on enhanced splitting-merging awareness tactics

Abstract

In this paper, we propose a new consensus clustering algorithm, which is based on an existing clustering paradigm, called enhanced splitting merging awareness tactics (E-SMART). The problem of determining the number of clusters, which affects many state-of-theart consensus clustering algorithms, is addressed by the proposed CoCE-SMART algorithm. The idea behind CoCE-SMART is that SMART is used repeatedly to one dataset, resulting in different clustering results, which might have different numbers of clusters. These SMART clustering results can be combined by clustering the centroids of all clusters as the estimate of real number of clusters can be determined from the SMART clustering results. Three benchmark datasets are utilised to assess the proposed algorithm. The experimental results strongly indicate that the proposed CoCE-SMART algorithm outperforms other state-of-the-art consensus clustering algorithms.