A fuzzy border-peeling clustering(FBP)algorithm is proposed.First,a density estimation method based on Cauchy kernel is used to calculate the densities of data points.Secondly,the boundary data are separated from the core data using the layer-by-layer peeling strategy.Thirdly,the reachability between the core data is used to achieve the core region clustering.Finally,a fuzzy assignment strategy is used to achieve the soft partitioning of the boundary data.A comparison is made between the fuzzy border-peeling clustering and 10 benchmark algorithms,including 6 density-based clustering algorithms and 4 fuzzy clustering algorithms,on artificial and real-world datasets.The experimental results show that on all datasets,FBP has the ARI(adjusted rand index)increased by 21％to 60％on average,and FBP has the NMI(normalized mutual information)increased by 12％to 47％on average.The border-peeling clustering algorithm optimized based on Cauchy kernel and fuzzy assignment strategy significantly improves the accuracy of clustering.
density-based clusteringborder-peeling clusteringfuzzy clusteringsoft clusteringCauchy kernel function
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