Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations - Sohan Lal Gupta; Vinod Kataria; Arpita Sharma; Vikram Khandelwal; Anjali Pandey; Vipin Kumar Gupta (International Journal of Solid State Innovations & Research, 2025)
K-Means clustering is a widely used unsupervised learning algorithm for partitioning a dataset into distinct clusters. Despite its popularity and simplicity, K-Means has several limitations, such as sensitivity to initial centroids, convergence to local minima, and inefficiency with large datasets. This paper reviews recent advancements aimed at addressing these challenges and enhancing the performance of the K-Means algorithm. Innovations include improved initialization methods, such as K-Means++, which significantly reduce the chances of poor clustering results by selecting more optimal starting centroids. Additionally, optimization techniques, such as using advanced optimization algorithms and parallel processing, have been developed to accelerate convergence and handle larger datasets more efficiently. We also explore hybrid approaches that combine K-Means with other clustering algorithms to achieve more accurate and robust clustering outcomes. These advancements collectively contribute to the enhanced performance, scalability, and robustness of the K-Means algorithm, making it more suitable for a wider range of applications in data analysis and machine learning.
Keywords: K-means; SSE; centroids; data clustering; data mining; distance metrices