NNGDPC: a kNNG-based density peaks clustering Online publication date: Thu, 14-Mar-2019
by Miao Li
International Journal of Collaborative Intelligence (IJCI), Vol. 2, No. 1, 2019
Abstract: Density peaks clustering (DPC) algorithm is a novel clustering algorithm based on density. It needs neither iterative process nor more parameters. However, the geometry of the distribution of the data has not been taken into account in the original algorithm. DPC does not perform well when clusters have different densities. In order to overcome this problem, we propose a novel density peaks clustering based on k-nearest neighbour graph called NNGDPC (kNNG-based density peaks clustering) which introduces the idea of the nearest neighbours (k-NNG) into DPC. By experiments on synthetic datasets, we show the power of the proposed algorithm. Experimental results show that our algorithm is feasible and effective.
Online publication date: Thu, 14-Mar-2019
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