Authors: Miao Li
Addresses: China Airborne Missile Academy, Luoyang 471003, China
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.
Keywords: data clustering; density peaks; k-nearest neighbour graph.
International Journal of Collaborative Intelligence, 2019 Vol.2 No.1, pp.1 - 15
Received: 18 Nov 2016
Accepted: 06 Dec 2016
Published online: 14 Mar 2019 *