The Python implementation of k-medoids.
from sklearn.metrics.pairwise import pairwise_distances
import numpy as np
import kmedoids
# 3 points in dataset
data = np.array([[1,1],
[2,2],
[10,10]])
# distance matrix
D = pairwise_distances(data, metric='euclidean')
# split into 2 clusters
M, C = kmedoids.kMedoids(D, 2)
print('medoids:')
for point_idx in M:
print( data[point_idx] )
print('')
print('clustering result:')
for label in C:
for point_idx in C[label]:
print('label {0}: {1}'.format(label, data[point_idx]))Output:
medoids:
[1 1]
[10 10]
clustering result:
label 0: [1 1]
label 0: [2 2]
label 1: [10 10]
This code is from:
Bauckhage C. Numpy/scipy Recipes for Data Science: k-Medoids Clustering[R]. Technical Report, University of Bonn, 2015.
Please cite the article if the code is used in your research.