Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5
The DBSCAN clustering algorithm has the following parameters:
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 n x p 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 n x 1 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. |
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 n x 1 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 1 x 1 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.