Page 128 - Data Science Algorithms in a Week

Clustering into K Clusters

Output for three clusters:

$ python k-means_clustering.py house_ownership2.csv 3 last
The total number of steps: 3
The history of the algorithm:
Step number 0: point_groups = [((0.09375, 0.2), 0), ((0.53125, 0.04), 0),
((0.875, 0.1), 1), ((1.0, 0.0), 1), ((0.25, 0.65), 2), ((0.15625, 0.48),
0), ((0.46875, 1.0), 2), ((0.375, 0.75), 2), ((0.0, 0.7), 0), ((0.625,
0.3), 1), ((0.9375, 0.5), 1)]
centroids = [(0.09375, 0.2), (1.0, 0.0), (0.46875, 1.0)]
Step number 1: point_groups = [((0.09375, 0.2), 0), ((0.53125, 0.04), 1),
((0.875, 0.1), 1), ((1.0, 0.0), 1), ((0.25, 0.65), 2), ((0.15625, 0.48),
0), ((0.46875, 1.0), 2), ((0.375, 0.75), 2), ((0.0, 0.7), 2), ((0.625,
0.3), 1), ((0.9375, 0.5), 1)]
centroids = [(0.1953125, 0.355), (0.859375, 0.225), (0.3645833333333333,
0.7999999999999999)]
Step number 2: point_groups = [((0.09375, 0.2), 0), ((0.53125, 0.04), 1),
((0.875, 0.1), 1), ((1.0, 0.0), 1), ((0.25, 0.65), 2), ((0.15625, 0.48),
0), ((0.46875, 1.0), 2), ((0.375, 0.75), 2), ((0.0, 0.7), 2), ((0.625,
0.3), 1), ((0.9375, 0.5), 1)]
centroids = [(0.125, 0.33999999999999997), (0.79375, 0.188), (0.2734375,
0.7749999999999999)]

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