Constraint-based clustering by fast search and find of density peaks

Abstract

Clustering by fast search and find of density peaks (CFDP) algorithm first proposed on Science is based on assumptions that the cluster center has the highest density among its neighbors and keeps a distance from other cluster centers. In CFDP algorithm, a local density metric and a minimal distance vector are first calculated for constructing a decision graph to select cluster centers. However, CFDP's performance is quite sensitive to parameter selection and relies on other prior knowledge. To solve the problem, this paper proposed a new clustering algorithm named constraint-based clustering by fast search and find of density peaks (CCFDP). In the proposed algorithm, several potential cluster centers are automatically formed and the structural information from constraints could be made full use of. CCFDP adopts a new method to obtain the density metric and the decision graph. After that, the decision graph is analyzed from different perspectives to help complete the final clustering. CCFDP is a semi-supervised robust clustering algorithm, combining semi-supervised constraints, density clustering and hierarchical clustering. Three synthetic and seven open datasets are used for testing its performance and robustness. The final results show that CCFDP outperforms other well-known constraint-based clustering algorithms.