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.
关键词
密度聚类/聚类分析/密度估计/局部密度峰值/互k近邻/侵蚀策略
Key words
density-based clustering/cluster analysis/density estimation/local density peak/mutual k-nearest neigh-bor/erosion strategy
万方数据