Batch Processing
Algorithm Parameters
The DBSCAN clustering algorithm has the following parameters:
Parameter  Default Valude  Description 

algorithmFPType  float  The floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 of additional memory,
which in worst case can be . However, in general, performance may be better. On GPU, the memorySavingMode flag can only be set to true .
You will get an error if the flag is set to false . 
resultsToCompute  0  The 64bit 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:

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 Algorithms.Input ID  Input 

data  Pointer to the numeric table with the data to be clustered. The input can be an object of any class derived from NumericTable . 
weights  Optional input. Pointer to the numeric table with weights of observations. The input can be an object of any class derived from NumericTable
except PackedTriangularMatrix , PackedSymmetricMatrix .By default all weights are equal to 1 .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 Algorithms.Result ID  Result 

assignments  Pointer to the numeric table with assignments of cluster indices to observations in the input data. Noise observations have the assignment equal to 1 . 
nClusters  Pointer to the numeric table with the total number of clusters found by the algorithm. 
coreIndices  Pointer to the numeric table with 1 column and arbitrary number of rows, containing indices of core observations. 
coreObservations  Pointer to the numeric table with p columns and arbitrary number of rows, containing core observations. 
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
.