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A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process

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  • Jiang, Jianhua
  • Chen, Yujun
  • Meng, Xianqiu
  • Wang, Limin
  • Li, Keqin

Abstract

Density Peaks Clustering (DPC) algorithm is a kind of density-based clustering approach, which can quickly search and find density peaks. However, DPC has deficiency in assignment process, which is likely to trigger domino effect. Especially, it cannot process some non-spherical data sets such as Spiral. The research results indicate that assignment process appears to be the most significant step in deciding the success of the clustering performance. Therefore, we propose a density peaks clustering based on k nearest neighbors (DPC-KNN) which aims to overcome the weakness of DPC. The proposed DPC-KNN integrates the idea of k nearest neighbors into the distance computation and assignment process, which is more reasonable. It can be seen from experimental results that the DPC-KNN algorithm is more feasible and effective, compared with K-means, DBSCAN and DPC.

Suggested Citation

  • Jiang, Jianhua & Chen, Yujun & Meng, Xianqiu & Wang, Limin & Li, Keqin, 2019. "A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 702-713.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:702-713
    DOI: 10.1016/j.physa.2019.03.012
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    References listed on IDEAS

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    1. Jiang, Jianhua & Chen, Yujun & Hao, Dehao & Li, Keqin, 2019. "DPC-LG: Density peaks clustering based on logistic distribution and gravitation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 25-35.
    2. Jiang, Jianhua & Hao, Dehao & Chen, Yujun & Parmar, Milan & Li, Keqin, 2018. "GDPC: Gravitation-based Density Peaks Clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 345-355.
    3. Shi, Yongbin & Li, Le & Wang, Yougui & Chen, Jiawei & Stanley, H. Eugene, 2019. "A study of Chinese regional hierarchical structure based on surnames," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 169-176.
    4. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
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    1. Shi, Lingyuan & Yang, Xin & Chang, Ximing & Wu, Jianjun & Sun, Huijun, 2023. "An improved density peaks clustering algorithm based on k nearest neighbors and turning point for evaluating the severity of railway accidents," Reliability Engineering and System Safety, Elsevier, vol. 233(C).

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