ENHANCED UNSUPERVISED K-MEANS CLUSTERING ALGORITHM
DOI:
https://doi.org/10.29121/shodhkosh.v5.i1.2024.2867Keywords:
Dynamic Clustering, Optimal Clusters, Clustering, K-Means Clustering, Algorithms, Computational EfficiencyAbstract [English]
K-Means clustering is an unsupervised learning algorithm for distinguishing data into separate groups called clusters based on similarity. However, the need to specify the cluster count (K) beforehand highly affects the effectiveness of the algorithm, which can be challenging in practice. In our manuscript, we introduce an improved iteration of the K-Means algorithm, which incorporates the elbow method to autonomously identify the required number of clusters during the clustering procedure. Our approach also incorporates optimization techniques to improve computational efficiency. The experimental findings substantiate the efficacy of our refined algorithm in automatically identifying the precise count of clusters while reducing computational overhead compared to traditional methods.
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Copyright (c) 2024 Dr. Gowsic K, Mugunthan S, Sakthivel Logavaseekarapakther, Puviyarasu A, Mohammed Farook R
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