https://github.com/letiantian/kmedoids

[Unmaintained] The Python implementation of k-medoids.
https://github.com/letiantian/kmedoids

Last synced: about 2 months ago
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[Unmaintained] The Python implementation of k-medoids.

• Host: GitHub
• URL: https://github.com/letiantian/kmedoids
• Owner: letiantian
• Archived: true
• Created: 2016-03-08T08:17:08.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2020-07-08T02:15:52.000Z (about 4 years ago)
• Last Synced: 2024-05-19T23:35:39.033Z (4 months ago)
• Language: Python
• Homepage:
• Size: 3.91 KB
• Stars: 125
• Watchers: 6
• Forks: 86
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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README

        
# kmedoids

The Python implementation of [k-medoids](https://en.wikipedia.org/wiki/K-medoids). 

## Example

```python

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]

```

## License

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.