Huge News!Announcing our $40M Series B led by Abstract Ventures.Learn More
Socket
Sign inDemoInstall
Socket

cluster

Package Overview
Dependencies
Maintainers
1
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

cluster

  • 1.4.1.post3
  • PyPI
  • Socket score

Maintainers
1

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

FAQs


Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap
  • Changelog

Packages

npm

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc