Density Peak Clustering Algorithm based on the Nearest Neighbor
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.106How to use a DOI?
- Clustering; density peaks; nearest neighbor; noise nodes.
- Clustering by fast search and find of density peaks is a new kind of density-based clustering algorithm, which can find the cluster center quickly and accurately by assuming that the cluster center has a high local density and is far away from other cluster centers. However, this clustering algorithm still has limitations in the non-central node allocation strategy and identifying anomalies. We improve on this algorithm and propose the NN-DPC algorithm with a new non-central node allocation strategy that does not rely on the cut-off distance and a method for identifying noise nodes. The experimental results show that the NN-DPC algorithm is more applicable and can identify noise nodes more accurately than the original clustering algorithm.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Bangyu Tong PY - 2019/04 DA - 2019/04 TI - Density Peak Clustering Algorithm based on the Nearest Neighbor PB - Atlantis Press SP - 665 EP - 670 SN - 2352-538X UR - https://doi.org/10.2991/icmeit-19.2019.106 DO - https://doi.org/10.2991/icmeit-19.2019.106 ID - Tong2019/04 ER -