Density peaks clustering based on k-nearest neighbors and self-recommendation

Abstract

Density peaks clustering (DPC) model focuses on searching density peaks and clustering data with arbitrary shapes for machine learning. However, it is difficult for DPC to select a cut-off distance in the calculation of a local density of points, and DPC easily ignores the cluster centers with lower density in datasets with variable densities. In addition, for clusters with complex shapes, DPC selects only one cluster center for a cluster, meaning that the structure of the whole cluster is not fully reflected. To overcome these drawbacks, this paper presents a novel DPC model that merges microclusters based on k-nearest neighbors (kNN) and self-recommendation, called DPC-MC for short. First, the kNN-based neighbourhood of point is defined and the mutual neighbour degree of point is presented in this neighbourhood, and then a new local density based on the mutual neighbour degree is proposed. This local density does not need to set the cut-off distance manually. Second, to address the artificial setting of cluster centers, a self-recommendation strategy for local centers is provided. Third, after the selection of multiple local centers, the binding degree of microclusters is developed to quantify the combination degree between a microcluster and its neighbour clusters. After that, homogeneous clusters are found according to the binding degree of microclusters during the process of deleting boundary points layer by layer. The homologous clusters are merged, the points in the abnormal clusters are reallocated, and then the clustering process ends. Finally, the DPC-MC algorithm is designed, and nine synthetic datasets and twenty-seven real-world datasets are used to verify the effectiveness of our algorithm. The experimental results demonstrate that the presented algorithm outperforms other compared algorithms in terms of several evaluation metrics for clustering.