Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 9, Issue 8, Pages 1335-1349Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-017-0648-x
Keywords
Data clustering; Density peaks clustering; Geodesic distances
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Funding
- National Natural Science Foundation of China [61379101, 61672522]
- National Key Basic Research Program of China [2013CB329502]
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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 multi-manifold structures. To handle this drawback, we propose a new density peaks clustering, i.e., density peaks clustering using geodesic distances (DPC-GD), 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 k-means algorithm and spectral clustering algorithm) and the original algorithm in accuracy and NMI. Experimental results show that our algorithm is feasible and effective.
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