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Partitioning methods in cluster analysis organize a set of objects into exclusive groups or clusters based on a specified number of clusters, optimizing an objective criterion for similarity within clusters and dissimilarity between them. The k-means algorithm is a widely used centroid-based technique that iteratively assigns objects to clusters based on their proximity to cluster centroids, while the k-medoids method uses actual objects as representatives to minimize sensitivity to outliers. Both methods h

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harsh1704gaur

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EN