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In 2014, Rodriguez and Laio proposed the density peak clustering which is the density based clustering. With only one parameter, called dc, the DPC uses the parameter to estimate the density of each data point and create a decision graph. From the decision graph, we can decide the number of clusters for each data set. Firstly, the local density of a point xi. Secondly, the d(xi) is defined as the minimum distance between the point i and any other point with higher density. Thirdly, the decision graph is constructed in which the x-axis and the y-axis are respectively the rho and delta values for every point in the data set
Finally, the clusters will detect by using peaks with a propagation label process. The detail steps of the DPC are presented in Algorithm 1. |
IEEE/ICACT20220068 Slide.07
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