Density-based clustering is a classical algorithm in cluster analysis,which can find non-spherical clusters without specifying the number of clusters in advance.In the real-world scene,there are still some issues,including unclear boundaries between clusters,varying densities of data,and complex cluster shapes.Most existing density-based clustering algorithms do not tackle these problems in a unified way.We counter this difficulty by taking inspiration from the natural erosion phenomenon to present erosion clustering(EC).Firstly,the proposed dynamic density evaluation method is integrated into the erosion strategy,which identifies and removes the data on the cluster boundary layer by layer,revealing the cores of the latent clusters.After that,a mutual-reachability-graph-based clustering is used to group the core data.Finally,the allocation strategy based on the local density peak is designed to associate the eroded data to different clusters.The experimental results on 12 benchmark datasets demonstrate that the clustering performance of the proposed EC algrithm is improved by an average of 96%,53%,and 36%in the adjusted Rand index,adjusted mutual information,and F1 score,respectively,comparing with the other seven algrithms.
density-based clusteringcluster analysisdensity estimationlocal density peakmutual k-nearest neigh-borerosion strategy
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