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13-11-2019 | Original Article | Issue 6/2020

International Journal of Machine Learning and Cybernetics 6/2020

Mk-NNG-DPC: density peaks clustering based on improved mutual K-nearest-neighbor graph

Journal:
International Journal of Machine Learning and Cybernetics > Issue 6/2020
Authors:
Jian-cong Fan, Pei-ling Jia, Linqiang Ge
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Abstract

Clustering by fast search and detection of density peaks (DPC, Density Peaks Clustering) is a relatively novel clustering algorithm published in the Science journal. As a density-based clustering algorithm, DPC produces better clustering results while using less parameters than other relevant algorithms. However, we found that the DPC algorithm does not perform well if clusters with different densities are very close. To address this problem, we propose a new DPC algorithm by incorporating an improved mutual k-nearest-neighbor graph (Mk-NNG) into DPC. Our Mk-NNG-DPC algorithm leverages the distance matrix of data samples to improve the Mk-NNG, and then utilizes DPC to constrain and select cluster centers. The proposed Mk-NNG-DPC algorithm ensures an instance to be allocated to the fittest cluster. Experimental results on synthetic and real world datasets show that our Mk-NNG-DPC algorithm can effectively and efficiently improve clustering performance, even for clusters with arbitrary shapes.

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