https://github.com/alexprengere/medoids

K-medoids implementation.
https://github.com/alexprengere/medoids

clustering kmeans python

Last synced: 3 months ago
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K-medoids implementation.

• Host: GitHub
• URL: https://github.com/alexprengere/medoids
• Owner: alexprengere
• License: apache-2.0
• Created: 2014-03-28T15:20:15.000Z (almost 11 years ago)
• Default Branch: master
• Last Pushed: 2018-10-20T17:19:13.000Z (about 6 years ago)
• Last Synced: 2024-10-03T19:47:07.358Z (3 months ago)
• Topics: clustering, kmeans, python
• Language: Python
• Size: 31.3 KB
• Stars: 19
• Watchers: 4
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README
  • License: LICENSE

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README

        
=========

K-Medoids

=========

This module implements a K-medoids algorithm using as input a list of points

and a distance function, like ``distance(p, q) = ||q - p||``:

.. code-block:: python

    >>> points = [1, 2, 3, 4, 5, 6, 7]

    >>> def distance(a, b):

    ...     return abs(b - a)

Just import the main ``k_medoids`` function:

.. code-block:: python

    >>> from medoids import k_medoids

    >>> diameter, medoids = k_medoids(points, k=2, distance, spawn=2) #doctest: +SKIP

    * New chosen kernels: [2, 3]

    * Iteration over after 3 steps, max diameter 3

    * New chosen kernels: [1, 2]

    * Iteration over after 4 steps, max diameter 3

    ~~ Spawn end: min of max diameters 3.000 for medoids: [Medoid(2, [1, 2, 3]), Medoid(5, [4, 5, 6, 7])]

There is also a ``k_medoids_auto_k`` which increases automatically the number of clusters

until we have homogeneous clusters:

.. code-block:: python

    >>> from medoids import k_medoids_auto_k

    >>> diameter, medoids = k_medoids_auto_k(points, diam_max=3, distance, spawn=3) #doctest: +SKIP

    * New chosen kernels: [2]

    * Iteration over after 2 steps, max diameter 6

    * New chosen kernels: [6]

    * Iteration over after 2 steps, max diameter 6

    * New chosen kernels: [2]

    * Iteration over after 2 steps, max diameter 6

    ~~ Spawn end: min of max diameters 6.000 for medoids: [Medoid(4, [1, 2, 3, 4, 5, 6, 7])]

    *** Diameter too big 6.000 > 3.000

    *** Now trying 2 clusters

    * New chosen kernels: [6, 2]

    * Iteration over after 2 steps, max diameter 3

    * New chosen kernels: [2, 6]

    * Iteration over after 1 steps, max diameter 3

    * New chosen kernels: [2, 1]

    * Iteration over after 3 steps, max diameter 4

    ~~ Spawn end: min of max diameters 3.000 for medoids: [Medoid(5, [4, 5, 6, 7]), Medoid(2, [1, 2, 3])]

    *** Diameter ok 3.000 <= 3.000

    *** Stopping, 2 clusters enough (7 points initially)