Page 141 - Data Science Algorithms in a Week

Clustering into K Clusters

0.0), 1), ((12.0, 0.0), 1), ((11.0, 0.0), 1)]
centroids = [(0.0, 0.0), (12.0, 0.0), (5.0, 0.0)]
Step number 1: point_groups = [((0.0, 0.0), 0), ((2.0, 0.0),
0), ((5.0, 0.0), 2), ((4.0, 0.0), 2), ((8.0, 0.0), 2), ((10.0,
0.0), 1), ((12.0, 0.0), 1), ((11.0, 0.0), 1)]
centroids = [(1.0, 0.0), (11.0, 0.0), (5.666666666666667, 0.0)]
For 4 clusters:

$ python k-means_clustering.py problem5_2.csv 4 last
The total number of steps: 2
The history of the algorithm:
Step number 0: point_groups = [((0.0, 0.0), 0), ((2.0, 0.0),
0), ((5.0, 0.0), 2), ((4.0, 0.0), 2), ((8.0, 0.0), 3), ((10.0,
0.0), 1), ((12.0, 0.0), 1), ((11.0, 0.0), 1)]
centroids = [(0.0, 0.0), (12.0, 0.0), (5.0, 0.0), (8.0, 0.0)]
Step number 1: point_groups = [((0.0, 0.0), 0), ((2.0, 0.0),
0), ((5.0, 0.0), 2), ((4.0, 0.0), 2), ((8.0, 0.0), 3), ((10.0,
0.0), 1), ((12.0, 0.0), 1), ((11.0, 0.0), 1)]
centroids = [(1.0, 0.0), (11.0, 0.0), (4.5, 0.0), (8.0, 0.0)]

b) We use the implemented algorithm again.
Input:

# source_code/5/problem5_2b.csv
2,2
2,5
10,4
3,5
7,3
5,9
2,8
4,10
7,4
4,4
5,8
9,3

Output for 2 clusters:

$ python k-means_clustering.py problem5_2b.csv 2 last
The total number of steps: 3
The history of the algorithm:
Step number 0: point_groups = [((2.0, 2.0), 0), ((2.0, 5.0),
0), ((10.0, 4.0), 1), ((3.0, 5.0), 0), ((7.0, 3.0), 1), ((5.0,
9.0), 1), ((2.0, 8.0), 0), ((4.0, 10.0), 0), ((7.0, 4.0), 1),
((4.0, 4.0), 0), ((5.0, 8.0), 1), ((9.0, 3.0), 1)]
centroids = [(2.0, 2.0), (10.0, 4.0)]

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