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K-Means is the most popular clustering algorithm that identifies K cluster centers called centroids with tentative coordinates in the data and iteratively assigns each observation to one of the centroids based on its features until the centroids converge. Data points are similar inside a cluster and different from the data points in the other clusters.


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