Enhancing Clustering Stability and Efficiency: A Framework for Optimizing K-means, K-medoids, and K-shape with Intelligent Algorithms

Abstract

Clustering methods like Kmeans often produce inconsistent results due to the random initialization of cluster centroids, even with optimizations such as Kmeans++, which improve centroid selection but fail to eliminate sensitivity to initialization randomness. Additionally, the choice of the number of clusters and distance metrics significantly impacts clustering performance. This paper proposes an enhanced framework combining intelligent optimization algorithms—Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), and Sine Cosine Algorithm (SCA)—to optimize clustering outcomes for Kmeans, Kmedoids, and Kshape. The framework also incorporates dimensionality reduction techniques, including Principal Component Analysis (PCA), Non-Negative Matrix Factorization (NNMF), and Singular Value Decomposition (SVD), to address high-dimensional data challenges. Experimental results on benchmark datasets demonstrate the proposed framework’s effectiveness, with SSA achieving the highest silhouette score of 0.68 and reducing runtime by 35% compared to traditional methods. This approach enhances clustering stability and accuracy, offering a robust solution for diverse applications.