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Perform a k-means Clustering (non-iterative algorithm) using k=2 randomly initialised centroids (cluster prototypes), and the Euclidean distance. At the moment I manage to understand you can use different algorithms to run K-means clustering (Lloyd, Forgy's, McQueen, Hattigan), but I understand all of the algorithm are iterative and I don't find or understand the non-iterative idea of non-iterative clustering. Please, could some one clarify me that? Thanks |
