Density peaks clustering using geodesic distances
International Journal of Machine Learning and Cybernetics, ISSN: 1868-808X, Vol: 9, Issue: 8, Page: 1335-1349
2018
- 62Citations
- 17Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85050085809&origin=inward; http://dx.doi.org/10.1007/s13042-017-0648-x; http://link.springer.com/10.1007/s13042-017-0648-x; http://link.springer.com/content/pdf/10.1007/s13042-017-0648-x.pdf; http://link.springer.com/article/10.1007/s13042-017-0648-x/fulltext.html; https://dx.doi.org/10.1007/s13042-017-0648-x; https://link.springer.com/article/10.1007/s13042-017-0648-x
Springer Science and Business Media LLC
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