Density peaks clustering based on superior nodes and fuzzy correlation

Abstract

The Density Peaks Clustering (DPC) algorithm is simple and efficient, but still has a few limitations. For example, DPC needs manual selection of clustering centers and may miss the correct cluster when searching for denser nearest neighbors, which may cause incorrect allocation of data points. To address these limitations, this work proposes a novel density peaks clustering algorithm based on superior nodes and fuzzy correlation (DPC-SNFC). Reverse nearest neighbors are used first to find the nearest point with a higher density as the superior node. Fuzzy correlation is then applied to construct connectivity subgraphs without using clustering centers. The connectivity subgraphs can identify the different clusters. Extensive experiments are conducted using 12 synthetic datasets and 10 real datasets and using 6 state-of-the-art baseline algorithms. The experimental results show that the proposed DPC-SNFC algorithm outperforms the baseline algorithms, which validates its efficiency.