K-Means Clustering Algorithm Based on GEO Optimization

This paper proposes a k-means algorithm enhanced with GEO (Golden Eagle Optimizer) optimization to address the challenges encountered by traditional k-means algorithm, such as being highly sensitive to initial values and prone to getting stuck in local optima during clustering. By incorporating GEO's attack and cruising vectors into the original loss function and iterative process of k-means, our algorithm enhances its exploratory capability while retaining its original clustering prowess. Theoretical analysis and simulation results demonstrate that our method can further minimize the loss function and exhibit superior clustering performance.