Optimizing K-Means Clustering: A Comparative Study of Optimization Algorithms For Convergence And Efficiency

Abstract

The K-Means clustering algorithm is a widely used technique for grouping data into clusters, with applications spanning various domains. This study presents a comparative investigation into the optimization of K-Means clustering through the evaluation of different optimization algorithms. The primary focus is on enhancing the convergence speed and computational efficiency of the K-Means algorithm, with implications for diverse real-world scenarios. The research systematically examines a range of optimization techniques, including gradient descent, stochastic gradient descent, and metaheuristic algorithms such as genetic algorithms and simulated annealing. A comprehensive analysis of convergence speed, clustering quality, and computational efficiency is conducted across these algorithms. By assessing their performance on diverse datasets, the study aims to provide insights into the trade-offs between different optimization strategies and their implications for practical clustering tasks. The results reveal distinct convergence patterns, highlighting the advantages and limitations of each optimization algorithm. Gradient-based approaches demonstrate rapid convergence but susceptibility to local optima, while stochastic gradient descent and metaheuristic algorithms exhibit a balance between exploration and exploitation. The findings shed light on the interplay between optimization techniques, convergence speed, and clustering quality, offering valuable guidance for practitioners seeking to optimize K-Means clustering according to specific dataset characteristics and computational requirements. This comparative study contributes to the broader understanding of optimizing K-Means clustering algorithms and aids researchers and practitioners in selecting suitable optimization strategies for efficient and effective data clustering in real-world applications.