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Batch Processing
================

Algorithm Parameters
********************

The DBSCAN clustering algorithm has the following parameters:

.. tabularcolumns::  |\Y{0.15}|\Y{0.15}|\Y{0.7}|

.. list-table:: Algorithm Parameters for DBSCAN (Batch Processing)
   :widths: 10 10 60
   :header-rows: 1
   :class: longtable

   * - Parameter
     - Default Valude
     - Description
   * - ``algorithmFPType``
     - ``float``
     - The floating-point type that the algorithm uses for intermediate computations. Can be ``float`` or ``double``.
   * - ``method``
     - ``defaultDense``
     - Available methods for computation of DBSCAN algorithm:

       - ``defaultDense`` – uses brute-force for neighborhood computation

   * - ``epsilon``
     - Not applicable
     - The maximum distance between observations lying in the same neighborhood.
   * - ``minObservations``
     - Not applicable
     - The number of observations in a neighborhood for an observation to be considered as a :term:`core <core observation>` one.
   * - ``memorySavingMode``
     - ``false``
     - If flag is set to false, all neighborhoods will be computed and stored prior to clustering.
       It will require up to :math:`O(|\text{sum of sizes of all observations' neighborhoods}|)` of additional memory,
       which in worst case can be :math:`O(|\text{number of observations}|^2)`. However, in general, performance may be better.

       .. note::
          On GPU, the ``memorySavingMode`` flag can only be set to ``true``.
          You will get an error if the flag is set to ``false``.

   * - ``resultsToCompute``
     - :math:`0`
     - The 64-bit integer flag that specifies which extra characteristics of the DBSCAN algorithm to compute.

       Provide one of the following values to request a single characteristic or
       use bitwise OR to request a combination of the characteristics:

       - ``computeCoreIndices`` for indices of core observations
       - ``computeCoreObservations`` for core observations

Algorithm Input
***************

The DBSCAN algorithm accepts the input described below.
Pass the ``Input ID`` as a parameter to the methods that provide input for your algorithm.
For more details, see :ref:`algorithms`.

.. tabularcolumns::  |\Y{0.2}|\Y{0.8}|

.. list-table:: Algorithm Input for DBSCAN (Batch Processing)
   :widths: 10 60
   :header-rows: 1
   :class: longtable

   * - Input ID
     - Input
   * - ``data``
     - Pointer to the :math:`n \times p` numeric table with the data to be clustered.

       .. note:: The input can be an object of any class derived from ``NumericTable``.

   * - ``weights``
     - Optional input. Pointer to the :math:`n \times 1` numeric table with weights of observations.

       .. note::

         The input can be an object of any class derived from ``NumericTable``
         except ``PackedTriangularMatrix``, ``PackedSymmetricMatrix``.

         By default all weights are equal to :math:`1`.

       .. note::

         This parameter is ignored on GPU.

Algorithm Output
****************

The DBSCAN algorithms calculates the results described below.
Pass the ``Result ID`` as a parameter to the methods that access the result of your algorithm.
For more details, see :ref:`algorithms`.

.. tabularcolumns::  |\Y{0.2}|\Y{0.8}|

.. list-table:: Algorithm Output for DBSCAN (Batch Processing)
   :widths: 10 60
   :header-rows: 1
   :class: longtable

   * - Result ID
     - Result
   * - ``assignments``
     - Pointer to the :math:`n \times 1` numeric table with assignments of cluster indices to observations in the input data.

       :term:`Noise observations <noise observation>` have the assignment equal to :math:`-1`.

   * - ``nClusters``
     - Pointer to the :math:`1 \times 1` numeric table with the total number of clusters found by the algorithm.
   * - ``coreIndices``
     - Pointer to the numeric table with :math:`1` column and arbitrary number of rows, containing indices of core observations.
   * - ``coreObservations``
     - Pointer to the numeric table with :math:`p` columns and arbitrary number of rows, containing core observations.

.. note::

    By default, this result is an object of the ``HomogenNumericTable`` class,
    but you can define the result as an object of any class derived from ``NumericTable``
    except ``PackedTriangularMatrix``, ``PackedSymmetricMatrix``, and ``CSRNumericTable``.