A Comprehensive Study on Density Peak Clustering and its Variants
Keywords:Density peak clustering (DPC), cut-off distance parameter, homogeneity, completeness, silhouette coefficient
Clustering is a technique used to group similar datapoints/samples. Similar group of datapoints can be formed by using distance measure or by density. Density peak clustering (DPC) groups datapoints based on the density. This paper shows variations and improvements of DPC and also the performance of DPC over other clustering algorithms. This paper also addresses the problem in DPC with random selection of cut-off distance parameter(dc). Local density of the datapoint is calculated based on dc. The improper selection of dc leads to wrong clustering results. The issue in the random choice of dc is addressed by using gini index or Gaussian function to make a valid guess on dc.. Here we have chosen homogeneity, completeness, silhouette coefficient as the three parameters to compare results of DPC, DPC with gini index, DPC with gaussian function.
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