Batch Processing

Algorithm Input

The K-Means clustering 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.

inputCentroids

Pointer to the nClusters x p numeric table with the initial centroids. The input can be an object of any class derived from NumericTable.

Algorithm Parameters

The K-Means clustering algorithm has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

Available computation methods for K-Means clustering:

  • defaultDense - implementation of Lloyd's algorithm
  • lloydCSR - implementation of Lloyd's algorithm for CSR numeric tables

nClusters

Not applicable

The number of clusters. Required to initialize the algorithm.

maxIterations

Not applicable

The number of iterations. Required to initialize the algorithm.

accuracyThreshold

0.0

The threshold for termination of the algorithm.

gamma

1.0

The weight to be used in distance calculation for binary categorical features.

distanceType

euclidean

The measure of closeness between points (observations) being clustered. The only distance type supported so far is the Euclidian distance.

assignFlag

true

A flag that enables computation of assignments, that is, assigning cluster indices to respective observations.

Algorithm Output

The K-Means clustering algorithm 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.

assignments

Use when assignFlag=true. Pointer to the n x 1 numeric table with assignments of cluster indices to feature vectors in the input data. 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.

DEPRECATED: goalFunction

USE INSTEAD: objectiveFunction

Pointer to the 1 x 1 numeric table with the value of the goal function. 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 CSRNumericTable.

nIterations

Pointer to the 1 x 1 numeric table with the actual number of iterations done by the algorithm. 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.

Note

You can skip update of centroids and objectiveFunction in the result and compute assignments using original inputCentroids. To do this, set assignFlag to true and maxIterations to zero.

For more complete information about compiler optimizations, see our Optimization Notice.
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