K-means

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• Published Subscription Original Research 9 Apr 2025

  Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations

  By Sohan Lal Gupta, Vinod Kataria, Arpita Sharma, Vikram Khandelwal, Anjali Pandey, Vipin Kumar Gupta

  Abstract: 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 …

  Published in International Journal of Solid State Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 30–37 Read article →