Page 126 - Data Science Algorithms in a Week

Clustering into K Clusters

52 1 30000 0 non-owner

28 0.25 95000 0.65 owner
25 0.15625 78000 0.48 non-owner
35 0.46875 130000 1 owner
32 0.375 105000 0.75 owner

20 0 100000 0.7 non-owner
40 0.625 60000 0.3 owner
50 0.9375 80000 0.5 ?

Given the table, we produce the input file for the algorithm and execute it, clustering the
features into the two clusters.
Input:

# source_code/5/house_ownership2.csv
0.09375,0.2
0.53125,0.04
0.875,0.1
1,0
0.25,0.65
0.15625,0.48
0.46875,1
0.375,0.75
0,0.7
0.625,0.3
0.9375,0.5
Output for two clusters:

$ python k-means_clustering.py house_ownership2.csv 2 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), 0), ((0.15625, 0.48),
0), ((0.46875, 1.0), 0), ((0.375, 0.75), 0), ((0.0, 0.7), 0), ((0.625,
0.3), 1), ((0.9375, 0.5), 1)]
centroids = [(0.09375, 0.2), (1.0, 0.0)]
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), 0), ((0.15625, 0.48),
0), ((0.46875, 1.0), 0), ((0.375, 0.75), 0), ((0.0, 0.7), 0), ((0.625,
0.3), 1), ((0.9375, 0.5), 1)]
centroids = [(0.26785714285714285, 0.5457142857142857), (0.859375, 0.225)]
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), 0), ((0.15625, 0.48),

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