Algorithms
================

These are mainly related to distance and density.

**OPTICS Algorithm**

OPTICS, or 'Ordering Points To Identify Clustering Structure', is a
density based clustering algorithm that I developed for segmentation of 
LiDAR points. It was also `my first pull request <https://github.com/scikit-learn/scikit-learn/pull/1984/>`_.

My implementation of the OPTICS algorithm was `merged <https://github.com/scikit-learn/scikit-learn/pull/11547>`_
into sklearn for `release 0.21 <https://scikit-learn.org/stable/whats_new/v0.21.html#version-0-21-1>`_,
and is available in all current versions. You can find the module documentation `here <https://scikit-learn.org/stable/modules/generated/sklearn.cluster.OPTICS.html>`_, with more 
`descriptive and narrative documentation here <https://scikit-learn.org/stable/modules/clustering.html#optics>`_;
there's also `demo code <https://scikit-learn.org/stable/auto_examples/cluster/plot_optics.html#sphx-glr-auto-examples-cluster-plot-optics-py>`_.


.. toctree::
    :hidden:   
    :maxdepth: 4
	
    Benchmarking.ipynb
    OPTICS