Density peaks clustering using geodesic distances

Authors: Mingjing Du Shifei Ding Xiao Xu Yu Xue

Publish Date: 2017/03/02

Volume: 9, Issue: 8, Pages: 1335-1349

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Abstract

Density peaks clustering DPC algorithm is a novel clustering algorithm based on density It needs neither iterative process nor more parameters However it cannot effectively group data with arbitrary shapes or multimanifold structures To handle this drawback we propose a new density peaks clustering ie density peaks clustering using geodesic distances DPCGD which introduces the idea of the geodesic distances into the original DPC method By experiments on synthetic data sets we reveal the power of the proposed algorithm By experiments on image data sets we compared our algorithm with classical methods kernel kmeans algorithm and spectral clustering algorithm and the original algorithm in accuracy and NMI Experimental results show that our algorithm is feasible and effectiveThis work is supported by the National Natural Science Foundation of China Nos 61379101 and 61672522 the National Key Basic Research Program of China No 2013CB329502 The Priority Academic Program Development of Jiangsu Higer Education Institutions PAPD and the Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology CICAEET

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