it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans

Class LinearKMeansCluster

java.lang.Object

it.uniroma2.sag.kelp.data.clustering.Cluster

it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansCluster

All Implemented Interfaces:

Serializable

public class LinearKMeansCluster
extends Cluster

It is the instance of a Cluster for Linear Algorithms, intended as a set of objects, instantiated as Examples, grouped together according to a measure of similarity.
This class extends the it.uniroma2.sag.kelp.data.clustering.Cluster with the notion of Centroid.

Author:

Danilo Croce

See Also:

Serialized Form

Field Summary

Fields inherited from class it.uniroma2.sag.kelp.data.clustering.Cluster

examples

Constructor Summary

Constructors

Constructor and Description
LinearKMeansCluster()
LinearKMeansCluster(String label)

Method Summary

Methods

Modifier and Type	Method and Description
void	clear() This function clear the set of object inside the cluster
Vector	getCentroid()
void	setCentroid(Vector centroid)
void	updateCentroid(String representationName) The centroid is calculated as the mean of all Vector representations stored in the clusters.

Methods inherited from class it.uniroma2.sag.kelp.data.clustering.Cluster

add, getExamples, getLabel, setExamples, setLabel, size, sortAscendingOrder, sortDescendingOrder, toString

Methods inherited from class java.lang.Object

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

Constructor Detail

LinearKMeansCluster

public LinearKMeansCluster()

LinearKMeansCluster

public LinearKMeansCluster(String label)

Parameters:

label - The cluster label

Method Detail

clear

public void clear()

Description copied from class: Cluster

This function clear the set of object inside the cluster

Overrides:

clear in class Cluster

getCentroid

public Vector getCentroid()

Returns:

The cluster centroid

setCentroid

public void setCentroid(Vector centroid)

updateCentroid

public void updateCentroid(String representationName)

The centroid is calculated as the mean of all Vector representations stored in the clusters.

Parameters:

representationName - The name of the rapresentation considered in the Mean