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Density Peak Clustering Based on Cumulative Nearest Neighbors Degree and Micro Cluster Merging

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A Correction to this article was published on 19 November 2019

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Abstract

Rodriguez et al. published an algorithm called clustering by fast search and find of density peaks (DPC) in Science in June 2014. It can quickly search the density peaks and cluster the datasets efficiently. However, there are some drawbacks. First, the local density definition is simple for the datasets with both dense clusters and sparse clusters; the density peaks cannot be found correctly using the two local density definition methods. Second, there is poor assignment fault tolerance, if a point is misallocated, the subsequent assignment will further amplify the error, which will have a serious impact on the clustering results. To solve the problems, a new clustering method, density peak clustering based on cumulative nearest neighbors degree and micro cluster merging, is proposed. The proposed method improves the DPC algorithm in two ways, the one is that the method defines a new local density to solve the defect of the DPC algorithm; the other one is that the graph degree linkage is combined with the DPC to alleviate the problem of distribution errors. The experiments on synthetic and real-world datasets show that the proposed method outperforms DPC, DBSCAN, OPTICS, AP, K-Means and other DPC variant algorithms.

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  • 19 November 2019

    The Publisher regrets an error on the printed front cover of the October 2019 issue. The issue numbers were incorrectly listed as Volume 91, Nos. 10-12, October 2019. The correct number should be: "Volume 91, No. 10, October 2019"

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Acknowledgments

This research was supported by the National Natural Science Foundation of China under Grant (Nos. 71433003, 51669014), the Science Fund for Distinguished Young Scholars of Jiangxi Province under Grant (No. 2018ACB21029).

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Authors and Affiliations

  1. College of Computer and Information Engineering, Hohai University, Nanjing, 211100, China

    Lizhong Xu, Jia Zhao, Aiye Shi & Zhe Chen

  2. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Jia Zhao & Zhanfeng Yao

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  1. Lizhong Xu
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Correspondence to Lizhong Xu.

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Xu, L., Zhao, J., Yao, Z. et al. Density Peak Clustering Based on Cumulative Nearest Neighbors Degree and Micro Cluster Merging. J Sign Process Syst 91, 1219–1236 (2019). https://doi.org/10.1007/s11265-019-01459-4

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