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A novel clustering algorithm by adaptively merging sub-clusters based on the Normal-neighbor and Merging force

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Abstract

Clustering by fast search and find of density peaks (DPC) is a popular clustering method based on density and distance. In DPC, each non-center point’s cluster label is led by its nearest point with higher density, which may cause some misclassifications of non-center points and interfere with the choice of correct cluster centers in the decision graph. To avoid these defects, we propose a novel clustering algorithm that automatically generates clusters without using the decision graph based on the Normal-neighbor and Merging force (NM-DPC). We conduct a series of experiments on various challenging synthetic datasets. Experimental results demonstrate that NM-DPC can better identify clusters of complex shapes and automatically recognize the number of clusters.

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Acknowledgements

This work was supported by the National Science Foundation of P.R. China (Grants: 61873239) and Zhejiang Science Foundation (Grant:2020C03074).

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Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Guan Junyi , Sheng li, He Xiongxiong & Chen Jiajia

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  1. Guan Junyi
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  2. Sheng li
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  3. He Xiongxiong
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  4. Chen Jiajia
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Correspondence to Sheng li.

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Junyi , G., li, S., Xiongxiong, H. et al. A novel clustering algorithm by adaptively merging sub-clusters based on the Normal-neighbor and Merging force. Pattern Anal Applic 24, 1231–1248 (2021). https://doi.org/10.1007/s10044-021-00981-1

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