/usr/lib/python2.7/dist-packages/cluster-1.3.3.egg-info/PKG-INFO is in python-cluster 1.3.3-1.
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Name: cluster
Version: 1.3.3
Summary: UNKNOWN
Home-page: https://github.com/exhuma/python-cluster
Author: Michel Albert
Author-email: michel@albert.lu
License: LGPL
Description: DESCRIPTION
===========
.. image:: https://readthedocs.org/projects/python-cluster/badge/?version=latest
:target: http://python-cluster.readthedocs.org
:alt: Documentation Status
python-cluster is a "simple" package that allows to create several groups
(clusters) of objects from a list. It's meant to be flexible and able to
cluster any object. To ensure this kind of flexibility, you need not only to
supply the list of objects, but also a function that calculates the similarity
between two of those objects. For simple datatypes, like integers, this can be
as simple as a subtraction, but more complex calculations are possible. Right
now, it is possible to generate the clusters using a hierarchical clustering
and the popular K-Means algorithm. For the hierarchical algorithm there are
different "linkage" (single, complete, average and uclus) methods available.
Algorithms are based on the document found at
http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/
.. note::
The above site is no longer avaialble, but you can still view it in the
internet archive at:
https://web.archive.org/web/20070912040206/http://home.dei.polimi.it//matteucc/Clustering/tutorial_html/
USAGE
=====
A simple python program could look like this::
>>> from cluster import HierarchicalClustering
>>> data = [12,34,23,32,46,96,13]
>>> cl = HierarchicalClustering(data, lambda x,y: abs(x-y))
>>> cl.getlevel(10) # get clusters of items closer than 10
[96, 46, [12, 13, 23, 34, 32]]
>>> cl.getlevel(5) # get clusters of items closer than 5
[96, 46, [12, 13], 23, [34, 32]]
Note, that when you retrieve a set of clusters, it immediately starts the
clustering process, which is quite complex. If you intend to create clusters
from a large dataset, consider doing that in a separate thread.
For K-Means clustering it would look like this::
>>> from cluster import KMeansClustering
>>> cl = KMeansClustering([(1,1), (2,1), (5,3), ...])
>>> clusters = cl.getclusters(2)
The parameter passed to getclusters is the count of clusters generated.
.. image:: https://readthedocs.org/projects/python-cluster/badge/?version=latest
:target: http://python-cluster.readthedocs.org
:alt: Documentation Status
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Other Audience
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v2 (LGPLv2)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Information Analysis
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