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.. * Licensed under the Apache License, Version 2.0 (the "License");
.. * you may not use this file except in compliance with the License.
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.. highlight:: cpp
.. default-domain:: cpp

.. re-use for math equations:
.. |x| replace:: :math:`x`
.. |y| replace:: :math:`y`

.. _alg_dbscan:

======
DBSCAN
======

.. include:: ../../../includes/clustering/dbscan-introduction.rst

------------------------
Mathematical formulation
------------------------

.. _dbscan_c_math:

Computation
-----------
Given the set :math:`X = \{x_1 = (x_{11}, \ldots, x_{1p}), \ldots, x_n = (x_{n1}, \ldots, x_{np})\}`
of :math:`n` :math:`p`-dimensional feature vectors (further referred as observations),
a positive floating-point number ``epsilon`` and a positive integer ``minObservations``,
the problem is to get clustering assignments for each input observation, based on the definitions below [Ester96]_:
two observations |x| and |y| are considered to be in the same cluster if there is a :term:`core observation` :math:`z`,
and |x| and |y| are both :term:`reachable` from :math:`z`.

Each cluster gets a unique identifier, an integer number from :math:`0` to :math:`\text{total number of clusters } – 1`.
Each observation is assigned an identifier of the :term:`cluster` it belongs to,
or :math:`-1` if the observation considered to be a :term:`noise observation`.

---------------------
Programming Interface
---------------------

Refer to :ref:`API Reference: DBSCAN <api_dbscan>`.

----------------
Distributed mode
----------------

The algorithm supports distributed execution in SMPD mode (only on GPU).

-------------
Usage example
-------------

.. include:: ../../../includes/clustering/dbscan-usage-examples.rst

--------
Examples
--------

.. include:: ../../../includes/clustering/dbscan-examples.rst