Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Density Peak Clustering Algorithm based on the Nearest Neighbor

Authors
Bangyu Tong
Corresponding Author
Bangyu Tong
Available Online April 2019.
DOI
https://doi.org/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.
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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.106How to use a DOI?
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  -