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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