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

Input

Centroid initialization for K-Means clustering accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm.

Algorithm Input for K-Means Initialization (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.

Parameters

The following table lists parameters of centroid initialization for K-Means clustering, which depend on the initialization method parameter method.

Algorithm Parameters for K-Means Initialization (Batch Processing)
Parameter	method	Default Value	Description
`algorithmFPType`	any	`float`	The floating-point type that the algorithm uses for intermediate computations. Can be `float` or `double`.
`method`	Not applicable	`defaultDense`	Available initialization methods for K-Means clustering: For CPU: `defaultDense` - uses first nClusters points as initial centroids `deterministicCSR` - uses first nClusters points as initial centroids for data in a CSR numeric table `randomDense` - uses random nClusters points as initial centroids `randomCSR` - uses random nClusters points as initial centroids for data in a CSR numeric table `plusPlusDense` - uses K-Means++ algorithm [Arthur2007] `plusPlusCSR` - uses K-Means++ algorithm for data in a CSR numeric table `parallelPlusDense` - uses parallel K-Means++ algorithm [Bahmani2012] `parallelPlusCSR` - uses parallel K-Means++ algorithm for data in a CSR numeric table For GPU: `defaultDense` - uses first nClusters points as initial centroids `randomDense` - uses random nClusters points as initial centroids
`nClusters`	any	Not applicable	The number of clusters. Required.
`nTrials`	`parallelPlusDense` `parallelPlusCSR`	1	The number of trails to generate all clusters but the first initial cluster. For details, see [Arthur2007], section 5
`oversamplingFactor`	`parallelPlusDense` `parallelPlusCSR`	0.5	A fraction of nClusters in each of nRounds of parallel K-Means++. L=nClusters*oversamplingFactor points are sampled in a round. For details, see [Bahmani2012], section 3.3.
`nRounds`	`parallelPlusDense` `parallelPlusCSR`	5	The number of rounds for parallel K-Means++. (L*nRounds) must be greater than nClusters. For details, see [Bahmani2012], section 3.3.
`engine`	any	SharePtr< engines:: mt19937:: Batch>()	Pointer to the random number generator engine that is used internally for random numbers generation.

Output

Centroid initialization for K-Means clustering calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm.

Algorithm Output for K-Means Initialization (Batch Processing)
Result ID	Result
`centroids`	Pointer to the $n C l u s t e r s \times p$ numeric table with the cluster centroids.

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 for PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.

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Intel® oneAPI Data Analytics Library Developer Guide and Reference

Batch Processing

Input

Parameters

Output