{ "info": { "author": "Michel Albert", "author_email": "michel@albert.lu", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Other Audience", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU Lesser General Public License v2 (LGPLv2)", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "DESCRIPTION\n===========\n\n.. image:: https://readthedocs.org/projects/python-cluster/badge/?version=latest\n :target: http://python-cluster.readthedocs.org\n :alt: Documentation Status\n\npython-cluster is a \"simple\" package that allows to create several groups\n(clusters) of objects from a list. It's meant to be flexible and able to\ncluster any object. To ensure this kind of flexibility, you need not only to\nsupply the list of objects, but also a function that calculates the similarity\nbetween two of those objects. For simple datatypes, like integers, this can be\nas simple as a subtraction, but more complex calculations are possible. Right\nnow, it is possible to generate the clusters using a hierarchical clustering\nand the popular K-Means algorithm. For the hierarchical algorithm there are\ndifferent \"linkage\" (single, complete, average and uclus) methods available.\n\nAlgorithms are based on the document found at\nhttp://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/\n\n.. note::\n The above site is no longer avaialble, but you can still view it in the\n internet archive at:\n https://web.archive.org/web/20070912040206/http://home.dei.polimi.it//matteucc/Clustering/tutorial_html/\n\n\nUSAGE\n=====\n\nA simple python program could look like this::\n\n >>> from cluster import HierarchicalClustering\n >>> data = [12,34,23,32,46,96,13]\n >>> cl = HierarchicalClustering(data, lambda x,y: abs(x-y))\n >>> cl.getlevel(10) # get clusters of items closer than 10\n [96, 46, [12, 13, 23, 34, 32]]\n >>> cl.getlevel(5) # get clusters of items closer than 5\n [96, 46, [12, 13], 23, [34, 32]]\n\nNote, that when you retrieve a set of clusters, it immediately starts the\nclustering process, which is quite complex. If you intend to create clusters\nfrom a large dataset, consider doing that in a separate thread.\n\nFor K-Means clustering it would look like this::\n\n >>> from cluster import KMeansClustering\n >>> cl = KMeansClustering([(1,1), (2,1), (5,3), ...])\n >>> clusters = cl.getclusters(2)\n\nThe parameter passed to getclusters is the count of clusters generated.\n\n\n.. image:: https://readthedocs.org/projects/python-cluster/badge/?version=latest\n :target: http://python-cluster.readthedocs.org\n :alt: Documentation Status", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/exhuma/python-cluster", "keywords": "", "license": "LGPL", "maintainer": "", "maintainer_email": "", "name": "cluster", "package_url": "https://pypi.org/project/cluster/", "platform": "", "project_url": "https://pypi.org/project/cluster/", "project_urls": { "Homepage": 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