oracle.dmt.odm.settings.algorithm
Class KMeansAlgorithmSettings

java.lang.Object
  |
  +--oracle.dmt.odm.MiningObject
        |
        +--oracle.dmt.odm.settings.algorithm.MiningAlgorithmSettings
              |
              +--oracle.dmt.odm.settings.algorithm.ClusteringAlgorithmSettings
                    |
                    +--oracle.dmt.odm.settings.algorithm.KMeansAlgorithmSettings

All Implemented Interfaces:

java.io.Serializable

public class KMeansAlgorithmSettings

extends ClusteringAlgorithmSettings

An instance of KMeansAlgorithmSettings is used to specify settings for the KMeans clustering algorithm. It allows a knowledgeable user to fine tune algorithm parameters. Generally, not all parameters must be specified, however, those specified are taken into account by the underlying DMS. ODM 9.2.0 implements a hierarchical version of the K-Means algorithm. The tree is grown one node at the time. The node with the largest distortion (sum of distance to the node's centroid) is split to increase the size of the tree until the desired number of clusters is reached.

Since:

9.2.0

See Also:

Serialized Form

Constructor Summary

KMeansAlgorithmSettings()
Creates a KMeansAlgorithmSettings default settings object.

KMeansAlgorithmSettings(float error, DistanceFunction distanceFunction)

KMeansAlgorithmSettings(int iterations, DistanceFunction distanceFunction)

KMeansAlgorithmSettings(int iterations, float error, DistanceFunction distanceFunction)
Creates a KMeansAlgorithmSettings object with the maximum number of K-Means iterations between splits set to iterations, the minimum percentual change in error between K-Means iterations set to error, and the distance function to be used to train a K-Means set to distanceFunction.

Method Summary

DistanceFunction	getDistanceFunction() Returns the DistanceFunction specified by a KMeansAlgorithmSettings object to train a K-Means ClusteringModel.
int	getMaxNumberOfIterations() Returns the maxNumberOfIterations specified by a KMeansAlgorithmSettings object to train a K-Means ClusteringModel.
float	getMinimumErrorTolerance() Returns the minimumErrorTolerance specified by a KMeansAlgorithmSettings object to train a K-Means ClusteringModel.
ClusteringStoppingCriterion	getStopCriterion()
void	setDistanceFunction(DistanceFunction distanceFunction) Sets the distance function to be used to train a K-Means ClusteringModel.
void	setMaxNumberOfIterations(int maxIter) Sets the maximum number of K-Means iterations between splits while training a K-Means ClusteringModel.
void	setMinErrorTolerance(float minError) Sets the minimum percentual change in error between K-Means iterations to consider that K-Means has converged.
void	setStopCriterion(ClusteringStoppingCriterion stopCriterion)

Methods inherited from class oracle.dmt.odm.settings.algorithm.MiningAlgorithmSettings

getMiningAlgorithm, getMiningAlgorithmName

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

KMeansAlgorithmSettings

public KMeansAlgorithmSettings(int iterations,
DistanceFunction distanceFunction)
                        throws InvalidArgumentException

Throws:

InvalidArgumentException -

• when iterations > 100 or iterations < 1, or
• when distanceFunction is null.

KMeansAlgorithmSettings

public KMeansAlgorithmSettings(float error,
DistanceFunction distanceFunction)
                        throws InvalidArgumentException

Parameters:

error - Minimum error tolerance. Recommended value: 0.05
Setting closer to .005 builds them model more slowly, but to .01 builds model faster, but perhaps with less accuracy.

distanceFunction - Distance function

Throws:

InvalidArgumentException -

• when error > 1 or error < 0, or
• when distanceFunction is null.

KMeansAlgorithmSettings

public KMeansAlgorithmSettings(int iterations,
                               float error,
DistanceFunction distanceFunction)
                        throws InvalidArgumentException

Creates a KMeansAlgorithmSettings object with the maximum number of K-Means iterations between splits set to iterations, the minimum percentual change in error between K-Means iterations set to error, and the distance function to be used to train a K-Means set to distanceFunction. iterations is a number between 1 and 100. error is a number between 0 and 1. Training stops after either the change in error between two consecutive iterations is less than error or the maximum number of iterations over the data in the buffer is greater than iterations.

Throws:

InvalidArgumentException -

• when error > 1 or error < 0,
• when iterations > 100 or iterations < 1, or
• when distanceFunction is null.

KMeansAlgorithmSettings

public KMeansAlgorithmSettings()

Creates a KMeansAlgorithmSettings default settings object.

Method Detail

getMinimumErrorTolerance

public float getMinimumErrorTolerance()

Returns the minimumErrorTolerance specified by a KMeansAlgorithmSettings object to train a K-Means ClusteringModel. The minimumErrorTolerance setting controls the minimum percentual change in error between K-Means iterations to considered that K-Means has converged. minimumErrorTolerance is a number between 0 and 1.

Returns:

Minimum error tolerance

getDistanceFunction

public DistanceFunction getDistanceFunction()

Returns the DistanceFunction specified by a KMeansAlgorithmSettings object to train a K-Means ClusteringModel.

Returns:

DistanceFunction for ClusteringModel training.

getStopCriterion

public ClusteringStoppingCriterion getStopCriterion()

Returns:

ClusteringStoppingCriterion for ClusteringModel training.

getMaxNumberOfIterations

public int getMaxNumberOfIterations()

Returns the maxNumberOfIterations specified by a KMeansAlgorithmSettings object to train a K-Means ClusteringModel. The maxNumberOfIterations setting controls the maximum number of K-Means iterations between splits while training a K-Means ClusteringModel. maxNumberOfIterations is a number between 1 and 100.

Returns:

Maximum number of iterations between splits

setMaxNumberOfIterations

public void setMaxNumberOfIterations(int maxIter)
                              throws InvalidArgumentException

Sets the maximum number of K-Means iterations between splits while training a K-Means ClusteringModel. maxIter is a number between 1 and 100.

Parameters:

maxIter - Maximum number of iterations

Throws:

InvalidArgumentException - when maxIter > 100 or maxIter < 1.

setMinErrorTolerance

public void setMinErrorTolerance(float minError)
                          throws InvalidArgumentException

Sets the minimum percentual change in error between K-Means iterations to consider that K-Means has converged. minError is a number between 0 and 1.

Parameters:

minError - Minimum percentual change in error

Throws:

InvalidArgumentException - when minError > 1 or minError < 0

setDistanceFunction

public void setDistanceFunction(DistanceFunction distanceFunction)

Sets the distance function to be used to train a K-Means ClusteringModel.

Parameters:

distanceFunction - Distance function used to train a K-Means ClusteringModel

setStopCriterion

public void setStopCriterion(ClusteringStoppingCriterion stopCriterion)