AHA-3WKM: The optimization of K-means with three-way clustering and artificial hummingbird algorithm

Xiying Chen, Caihui Liu, Bowen Lin, Jianying Lai, Duoqian Miao

Abstract: Highlights•AHA is introduced to address the problems of the sensitivity to initial cluster centers and the proneness to local optima. Hummingbirds are treated as data points, which dynamically update their strategies and effectively find the optimal cluster centers during multiple iterations.•A fitness function is designed based on the clustering principle of “birds of a feather flock together”, with the aim of simplifying calculations, which enhances the specificity and practicality of K-means algorithm.•An AHA-based three-way K-means clustering algorithm (i.e., AHA-3WKM) is proposed. The clustering process is initialized with cluster centers optimized by AHA, and the results are represented in three regions, which can capture the uncertainty within the datasets.

External IDs:dblp:journals/isci/ChenLLLM24