Volume 13, Issue 1, 2020, Pages 690 - 697
A Novel Density Peaks Clustering Algorithm Based on Local Reachability Density
Authors
Hanqing Wang1, Bin Zhou1, *, Jianyong Zhang2, Ruixue Cheng2
1School of Energy and Environment, Southeast University, Sipailou Road 2, Nanjing, Jiangsu, China
2School of Computing, Engineering and Digital Technologies, Teesside University, TS1 3BA, Middlesbrough, UK
*Corresponding author. Email: zhoubinde@seu.edu.cn
Corresponding Author
Bin Zhou
Received 29 February 2020, Accepted 19 May 2020, Available Online 16 June 2020.
- DOI
- 10.2991/ijcis.d.200603.001How to use a DOI?
- Keywords
- Clustering algorithm; Density peaks clustering; Local reachability density; Domino effect
- Abstract
A novel clustering algorithm named local reachability density peaks clustering (LRDPC) which uses local reachability density to improve the performance of the density peaks clustering algorithm (DPC) is proposed in this paper. This algorithm enhances robustness by removing the cutoff distance dc which is a sensitive parameter from the DPC. In addition, a new allocation strategy is developed to eliminate the domino effect, which often occurs in DPC. The experimental results confirm that this algorithm is feasible and effective.
- Copyright
- © 2020 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Hanqing Wang AU - Bin Zhou AU - Jianyong Zhang AU - Ruixue Cheng PY - 2020 DA - 2020/06/16 TI - A Novel Density Peaks Clustering Algorithm Based on Local Reachability Density JO - International Journal of Computational Intelligence Systems SP - 690 EP - 697 VL - 13 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200603.001 DO - 10.2991/ijcis.d.200603.001 ID - Wang2020 ER -