Page 135 - Data Science Algorithms in a Week

Clustering into K Clusters

We can observe that clustering into the 2 clusters divides books into religious ones, the ones
in the blue cluster and non-religious ones, the ones in the red cluster. Let us try to cluster
the books into the 3 clusters to observe how the algorithm would divide the data.

Output for 3 clusters:

$ python k-means_clustering.py
document_clustering/word_frequencies_money_god_scaled.csv 3 last
The total number of steps: 3
The history of the algorithm:
Step number 0: point_groups = [((0.0, 0.0406976744), 0), ((0.0,
0.0988372093), 0), ((0.125, 0.0581395349), 0), ((0.0, 0.1860465116), 0),
((0.0, 0.0348837209), 0), ((0.0, 0.1569767442), 0), ((0.0, 0.0348837209),
0), ((0.25, 0.3430232558), 0), ((0.25, 0.261627907), 0), ((0.125,
0.4011627907), 0), ((0.125, 1.0), 2), ((0.625, 0.0058139535), 1), ((1.0,
0.0), 1), ((0.5, 0.0058139535), 1), ((0.375, 0.0174418605), 0), ((0.5,
0.0174418605), 1), ((0.75, 0.0174418605), 1)]
centroids = [(0.0, 0.0406976744), (1.0, 0.0), (0.125, 1.0)]
Step number 1: point_groups = [((0.0, 0.0406976744), 0), ((0.0,
0.0988372093), 0), ((0.125, 0.0581395349), 0), ((0.0, 0.1860465116), 0),
((0.0, 0.0348837209), 0), ((0.0, 0.1569767442), 0), ((0.0, 0.0348837209),
0), ((0.25, 0.3430232558), 0), ((0.25, 0.261627907), 0), ((0.125,
0.4011627907), 0), ((0.125, 1.0), 2), ((0.625, 0.0058139535), 1), ((1.0,
0.0), 1), ((0.5, 0.0058139535), 1), ((0.375, 0.0174418605), 1), ((0.5,
0.0174418605), 1), ((0.75, 0.0174418605), 1)]
centroids = [(0.10227272727272728, 0.14852008456363636), (0.675,
0.0093023256), (0.125, 1.0)]
Step number 2: point_groups = [((0.0, 0.0406976744), 0), ((0.0,
0.0988372093), 0), ((0.125, 0.0581395349), 0), ((0.0, 0.1860465116), 0),
((0.0, 0.0348837209), 0), ((0.0, 0.1569767442), 0), ((0.0, 0.0348837209),
0), ((0.25, 0.3430232558), 0), ((0.25, 0.261627907), 0), ((0.125,
0.4011627907), 0), ((0.125, 1.0), 2), ((0.625, 0.0058139535), 1), ((1.0,
0.0), 1), ((0.5, 0.0058139535), 1), ((0.375, 0.0174418605), 1), ((0.5,
0.0174418605), 1), ((0.75, 0.0174418605), 1)]
centroids = [(0.075, 0.16162790697), (0.625, 0.01065891475), (0.125, 1.0)]

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