Author(s)

Chunyan Qiu, Yihe Zhang, Yang Liu, Jialing Han, Shuang Hu, Zhiyu Chen

Corresponding Author

Yang Liu

ABSTRACT

This paper proposes a density peak algorithm based on artificial bee colony optimization. The improved DPC algorithm can better realize the automatic identification and reasonable clustering of data points between clusters, and reduce the difficulty of selecting the value of the original density peak clustering (DPC) algorithm and the limitation of the neighboring principle aggregation operation in the low density area. This paper verifies the clustering effectiveness of the proposed algorithm using some known data sets and custom data sets. Clustering comparison experiments with K-Means, AP, and DPC algorithms on multiple classical datasets. The experimental results show that compared with the DPC algorithm, the proposed algorithm automatically recognizes and reasonably clusters data points between clusters; automatically identifies the cluster center points and clusters and automatically handles the advantages of randomly distributed data sets.

KEYWORDS

Density Peak Clustering, Artificial Bee Colony Optimization, K-Means, Cluster