The main steps in the k-means clustering algorithm are
- Assign each sample to the closest centroid, then calculate the new centroid.
- Calculate the centroids, then determine the appropriate stopping criterion depending on the number of centroids.
- Calculate the distances between the cluster centroids, then find the two closest centroids.
- Count the number of samples, then determine the initial centroids.
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