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What is K-Means Clustering Explained about?

K-means clustering is an unsupervised learning algorithm that groups data points into K number of clusters based on their characteristics. It works by assigning data points to the closest cluster centroid, where centroids are initial cluster centers. It then recalculates the centroid positions based on the new assignments and reassigns data points until centroids remain stable. For a given dataset with points A(2,4), B(2,3), C(5,2), D(6,2), E(5,2.5), F(2.5,3.5) and setting K to 2, the algorithm initially as

Author

MD. SHAHID-UL ISLAM

Language

EN