An adaptive clustering algorithm based on CFSFDP
Feng Yang, Jinming Cao, Kuang Zhou, Pengyan Zhang, Yongting Wang · 2018
The main idea of the recent proposed clustering by fast search and find of density peaks (CFSFDP) clustering algorithm depicts the cluster center with the local density and distance and it achieves significant effects in typical applications. But it can't select the cluster centers adaptively. Therefore, an improved CFSFDP algorithm is proposed in this paper, which determines the cluster centers by the Max-min algorithm. First, the Max-min algorithm is introduced to obtain the number of categories. Then the local density and distance information is used to determine the cluster centers as do in CFSFDP algorithm. It can not only adaptively obtain categories number of the data, but also obtain the corresponding clustering centers. The simulation results show that the proposed algorithm can find the number of categories and find the cluster centers. Meanwhile, it can find the cluster centers which are hard to be obtained through decision diagram.