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Density peaks clustering using geodesic distances

International Journal of Machine Learning and Cybernetics, ISSN: 1868-808X, Vol: 9, Issue: 8, Page: 1335-1349
2018
  • 62
    Citations
  • 0
    Usage
  • 17
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    62
    • Citation Indexes
      62
  • Captures
    17

Article Description

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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