Abstract: Aiming at  the limitations of the original density peaks clustering algorithm, including its neglect of inter-cluster density variations, requirement for predefining the number of clusters, and reliance on a single allocation strategy, we proposed a density peak clustering algorithm based on adaptive hierarchical shared neighbors. Firstly, we  calculated similarity between samples and redefined local density and relative distance by adaptively sharing  neighbors and hierarchically increasing  weights. Secondly, we introduced the second-order derivatives to identify inflection points and calculated the weighted triangular areas based on inflection point information to automatically select clustering centers. Finally,  we combined the similarity matrix with relative distance for  secondary allocation to reduce the effects of  chain reactions. Experimental results on nine artificial datasets and nine UCI real datasets show that the proposed algorithm generally outperforms the density peaks clustering algorithm and other improved algorithms in clustering performance, exhibiting higher accuracy and robustness, and is well-suited for clustering analysis of complex data distributions.

Key words: density peak clustering, hierarchical shared neighbor, local density, clustering center, assignment policy