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Welcome to python-cluster's documentation!
==========================================
Index
-----
.. toctree::
:maxdepth: 1
changelog
Introduction
------------
Implementation of cluster algorithms in pure Python.
As this is exacuted in the Python runtime, the code runs slower than similar
implementations in compiled languages. You gain however to run this on pretty
much any Python object. The different clustering methods have different
prerequisites however which are mentioned in the different implementations.
Example for K-Means Clustering
------------------------------
::
from cluster import KMeansClustering
data = [
(8, 2),
(7, 3),
(2, 6),
(3, 5),
(3, 6),
(1, 5),
(8, 1),
(3, 4),
(8, 3),
(9, 2),
(2, 5),
(9, 3)
]
cl = KMeansClustering(data)
cl.getclusters(2)
The above code would give the following result::
[
[(8, 2), (8, 1), (8, 3), (7, 3), (9, 2), (9, 3)],
[(3, 5), (1, 5), (3, 4), (2, 6), (2, 5), (3, 6)]
]
Example for Hierarchical Clustering
-----------------------------------
::
from cluster import HierarchicalClustering
data = [791, 956, 676, 124, 564, 84, 24, 365, 594, 940, 398,
971, 131, 365, 542, 336, 518, 835, 134, 391]
cl = HierarchicalClustering(data)
cl.getlevel(40)
The above code would give the following result::
[
[24],
[84, 124, 131, 134],
[336, 365, 365, 391, 398],
[676],
[594, 518, 542, 564],
[940, 956, 971],
[791],
[835],
]
Using :py:meth:`~cluster.method.hierarchical.HierarchicalClustering.getlevel()`
returns clusters where the distance between each cluster is no less than
*level*.
.. note::
Due to a bug_ in earlier releases, the elements of the input data *must be*
sortable!
.. _bug: https://github.com/exhuma/python-cluster/issues/11
API
---
.. toctree::
:maxdepth: 1
apidoc/cluster
apidoc/cluster.matrix
apidoc/cluster.method.base
apidoc/cluster.method.hierarchical
apidoc/cluster.method.kmeans
apidoc/cluster.util
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
|
