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Contents

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 \(n \times p\) 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 \(nClusters \times p\) 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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K-Means Clustering

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