it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans

Class KernelBasedKMeansEngine

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine

All Implemented Interfaces:

ClusteringAlgorithm

public class KernelBasedKMeansEngine
extends Object
implements ClusteringAlgorithm

Implements the Kernel Based K-means described in: Brian Kulis, Sugato Basu, Inderjit Dhillon, and Raymond Mooney. Semi-supervised graph clustering: a kernel approach. Machine Learning, 74(1):1-22, January 2009.

Author:

Danilo Croce

Constructor Summary

Constructors

Constructor and Description
KernelBasedKMeansEngine()
KernelBasedKMeansEngine(Kernel kernel, int k, int maxIterations)

Method Summary

Methods

Modifier and Type	Method and Description
float	calculateDistance(Example example, Cluster cluster) Estimate the distance of an example from the centroid
void	checkConsistency(int K, int inputSize)
ClusterList	cluster(Dataset dataset) It starts the clustering process exploiting the provided dataset
ClusterList	cluster(Dataset dataset, ExampleSelector seedSelector) It starts the clustering process exploiting the provided dataset
float	evaluateKernel(Example e1, Example e2)
int	getK()
Kernel	getKernel()
int	getMaxIterations()
void	setK(int k)
void	setKernel(Kernel kernel)
void	setMaxIterations(int maxIterations)

Methods inherited from class java.lang.Object

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

Constructor Detail

KernelBasedKMeansEngine

public KernelBasedKMeansEngine()

KernelBasedKMeansEngine

public KernelBasedKMeansEngine(Kernel kernel,
                       int k,
                       int maxIterations)

Parameters:

kernel - The kernel function

k - The number of expected clusters

maxIterations - The maximum number of iterations

Method Detail

calculateDistance

public float calculateDistance(Example example,
                      Cluster cluster)

Estimate the distance of an example from the centroid

Parameters:

example - An example

cluster - A cluster

Returns:

The distance

checkConsistency

public void checkConsistency(int K,
                    int inputSize)
                      throws Exception

Throws:

Exception

cluster

public ClusterList cluster(Dataset dataset)

Description copied from interface: ClusteringAlgorithm

It starts the clustering process exploiting the provided dataset

Specified by:

cluster in interface ClusteringAlgorithm

Returns:

the data instances grouped in clusters

cluster

public ClusterList cluster(Dataset dataset,
                  ExampleSelector seedSelector)

Description copied from interface: ClusteringAlgorithm

It starts the clustering process exploiting the provided dataset

Specified by:

cluster in interface ClusteringAlgorithm

seedSelector - the seed selector

Returns:

the data instances grouped in clusters

evaluateKernel

public float evaluateKernel(Example e1,
                   Example e2)

getK

public int getK()

getKernel

public Kernel getKernel()

getMaxIterations

public int getMaxIterations()

setK

public void setK(int k)

setKernel

public void setKernel(Kernel kernel)

setMaxIterations

public void setMaxIterations(int maxIterations)