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class Clustering:
def k_means_clustering(self, n, s=1.0):
"""
This method performs the K-means clustering algorithm on the data for n iterations. This involves updating the
centroids using the mean-shift heuristic n-times and reassigning the patterns to their closest centroids.
:param n: number of iterations to complete
:param s: the scaling factor to use when updating the centroids
pick on which has a better solution (according to some measure of cluster quality)
"""
for i in range(n):
self.assign_patterns()
self.update_centroids(s)
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