Density Peak Clustering Algorithm based on the Nearest Neighbor

DOI

10.2991/icmeit-19.2019.106How to use a DOI?

Keywords

Clustering; density peaks; nearest neighbor; noise nodes.

Abstract

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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bangyu Tong
PY  - 2019/04
DA  - 2019/04
TI  - Density Peak Clustering Algorithm based on the Nearest Neighbor
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 665
EP  - 670
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.106
DO  - 10.2991/icmeit-19.2019.106
ID  - Tong2019/04
ER  -