Welcome to K-Means Clustering’s documentation!¶
Contents:
This is a package for easily performing k-means clustering on a set of data. Unique to this implementation is its ability to specify the dimension up to which the data is clustered. For example:
>>> from kmeans import cluster
>>> import numpy as np
>>> np.random.seed(27) # For reproducible results
>>> data = np.random.random((15, 5)).round(3)
>>> data[0]
array([0.426, 0.815, 0.735, 0.868, 0.383])
>>> # Cluster using only first two dimensions
>>> clusters, centroids = cluster(data, k=3, ndim=2, tolerance=0.001)
>>> centroids
array([[0.9004 , 0.79 ],
[0.361375, 0.580125],
[0.801 , 0.143 ]])
>>> clusters # visually compare centroids with first two elements of each data entry.
{0: array([[0.979, 0.893, 0.21 , 0.742, 0.663],
[0.887, 0.858, 0.749, 0.87 , 0.187],
[0.966, 0.583, 0.092, 0.014, 0.837],
[0.915, 0.705, 0.387, 0.706, 0.923],
[0.755, 0.911, 0.242, 0.976, 0.304]]),
1: array([[0.426, 0.815, 0.735, 0.868, 0.383],
[0.326, 0.373, 0.794, 0.151, 0.17 ],
[0.081, 0.305, 0.783, 0.163, 0.071],
[0.221, 0.726, 0.849, 0.929, 0.736],
[0.477, 0.493, 0.595, 0.076, 0.117],
[0.288, 0.684, 0.52 , 0.877, 0.924],
[0.489, 0.596, 0.264, 0.992, 0.21 ],
[0.583, 0.649, 0.911, 0.122, 0.676]]),
2: array([[0.701, 0.181, 0.599, 0.415, 0.514],
[0.901, 0.105, 0.673, 0.87 , 0.561]])}
Installation¶
The package can be installed via PyPI:
$ python -m pip install kmeans-tjdwill
Issues¶
Create an issue on the GitHub page. Please be civil, professional, and kind.
License¶
This work is licensed under the MIT License.