Batch Processing

Intel® oneAPI Data Analytics Library Developer Guide and Reference

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ID 772611

Date 7/13/2023

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Batch Processing

Algorithm Parameters

The DBSCAN clustering algorithm has the following parameters:

Algorithm Parameters for DBSCAN (Batch Processing)
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 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 $O (\| sum of sizes of all observations' neighborhoods \|)$ of additional memory, which in worst case can be $O (\| number of observations \|^{2})$ . 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`	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 Algorithms.

Algorithm Input for DBSCAN (Batch Processing)
Input ID	Input
`data`	Pointer to the $n i m e s p$ 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 $n i m e s 1$ 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 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 Algorithms.

Algorithm Output for DBSCAN (Batch Processing)
Result ID	Result
`assignments`	Pointer to the $n i m e s 1$ 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 $1 i m e s 1$ 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.

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.

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Intel® oneAPI Data Analytics Library Developer Guide and Reference

Batch Processing

Algorithm Parameters

Algorithm Input

Algorithm Output