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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 ` 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 ` 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``.