An improved K-Means clustering based on differential evolution

Abstract

Randomly selecting the initial cluster center point in the K-means clustering algorithm can lead to the issue of local optima. The paper introduces an enhanced differential evolution algorithm for optimizing K-means clustering. Experimental results demonstrate the superiority of the improved K-means clustering, based on the proposed differential evolution, over other comparison algorithms. Notably, the optimized version exhibits exceptional performance in clustering Iris, Wine, and Glass datasets. The initial clustering center can improve the clustering accuracy in the later stage. Therefore, the proposed algorithm is more superior.