Density Peaks Clustering Algorithm Based on Mutual K-Nearest Neighbor
Density peaks clustering, a kind of clustering algorithm with simple principle and high efficiency, faces several challenges, such as disunity in density definition, easy error in cluster centers selection and"domino"phenomenon in sample allocation. To solve these problems, a density peaks clustering algorithm based on mutual K-nearest neighbor (MKDPC) is pro-posed. Firstly, an improved density is defined based on the mutual K-nearest neighbor of samples, which unifies the density definition method of DPC algorithm, and can effectively avoid the problem of cluster centers selection error of variable densi-ty datasets. Secondly, the shared mutual K-nearest neighbor and similarity between samples are defined based on mutual K-nearest neighbor, and then a multi-step sample allocation strategy is proposed, which can effectively overcome the"domino"phenomenon in the process of sample allocation. Experiments are carried out on synthetic datasets and real datasets, and the MKDPC algorithm is compared with other four alternative methods, with results substantiating its efficacy.
density peaks clusteringmutual K-nearest neighborlocal densityallocation strategy
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