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. 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.

Parameters

Centroid initialization for K-Means clustering has the following parameters:

Parameter	Default Value	Description
algorithmFPType	double	The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
method	defaultDense	Available initialization methods for K-Means clustering: defaultDense - uses first nClusters points as initial clusters deterministicCSR - uses first nClusters points as initial clusters for data in a CSR numeric table randomDense - uses random nClusters points as initial clusters randomCSR - uses random nClusters points as initial clusters for data in a CSR numeric table For more details, see the algorithm description.
nClusters	Not applicable	The number of clusters. Required.
seed	777	The seed for generating random numbers.

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. For more details, see Algorithms.

Result ID	Result
centroids	Pointer to the nClusters x `p` numeric table with the cluster centroids. 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.