Page 129 - Data Science Algorithms in a Week

Clustering into K Clusters

The red cluster has stayed the same. Let us therefore cluster the data into the 4 clusters.

Output for four clusters:
$ python k-means_clustering.py house_ownership2.csv 4 last
The total number of steps: 2
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), 3), ((0.15625, 0.48),
3), ((0.46875, 1.0), 2), ((0.375, 0.75), 2), ((0.0, 0.7), 3), ((0.625,
0.3), 1), ((0.9375, 0.5), 1)]
centroids = [(0.09375, 0.2), (1.0, 0.0), (0.46875, 1.0), (0.0, 0.7)]
Step number 1: 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), 3), ((0.15625, 0.48),
3), ((0.46875, 1.0), 2), ((0.375, 0.75), 2), ((0.0, 0.7), 3), ((0.625,
0.3), 1), ((0.9375, 0.5), 1)]
centroids = [(0.3125, 0.12000000000000001), (0.859375, 0.225), (0.421875,
0.875), (0.13541666666666666, 0.61)]

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