A novel density peaks clustering with sensitivity of local density and density-adaptive metric

  • Mingjing Du
  • Shifei Ding
  • Yu Xue
  • Zhongzhi Shi
Regular Paper


The density peaks (DP) clustering approach is a novel density-based clustering algorithm. On the basis of the prior assumption of consistency for semi-supervised learning problems, we further make the assumptions of consistency for density-based clustering. The first one is the assumption of the local consistency, which means nearby points are likely to have the similar local density; the second one is the assumption of the global consistency, which means points on the same high-density area (or the same structure, i.e., the same cluster) are likely to have the same label. According to the first assumption, we provide a new option based on the sensitivity of the local density for the local density. In addition, we redefine \( \delta \) and redesign the assignation strategy based on a new density-adaptive metric according to the second assumption. We compare the performance of our algorithm with traditional clustering schemes, including DP, K-means, fuzzy C-means, Gaussian mixture model, and self-organizing maps. Experiments on different benchmark data sets demonstrate the effectiveness of the proposed algorithm.


Clustering analysis Density peaks clustering Sensitivity of local density Density-adaptive distance 



This work is supported by the National Natural Science Foundation of China (Nos. 61672522 and 61379101), and the National Key Basic Research Program of China (No. 2013CB329502).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Mingjing Du
    • 1
  • Shifei Ding
    • 1
    • 2
  • Yu Xue
    • 3
  • Zhongzhi Shi
    • 2
  1. 1.School of Computer Science and TechnologyChina University of Mining and TechnologyXuzhouChina
  2. 2.Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  3. 3.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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