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kddml.Core.DataMining.Clustering.CentroidBasedCluster Class Reference

Inheritance diagram for kddml.Core.DataMining.Clustering.CentroidBasedCluster:

kddml.Core.DataMining.Clustering.Cluster kddml.Core.DataMining.Clustering.CentroidBasedClusterManager kddml.Core.DataMining.Clustering.ClusterManager kddml.Core.DataMining.Clustering.ClusterManager List of all members.

Public Member Functions

 CentroidBasedCluster (String name, double[] centroid)
boolean isDistributionBased ()
boolean isCentroidBased ()
Object getSeedAsInstance () throws ClusteringModelException
double[] getSeedCoordinate ()
String toString ()
 return null;

Protected Member Functions

Element toXML ()

Detailed Description

Center-based (or distance-based) cluster need a distance or similarity measurement based on which they try to group those most similar objects into one cluster. Example: K-means, CLARANS. A center-based cluster is always defined by their centroid. Statistical information are optional.

Title: KDDML

Description: Knowledge Discovery in Database Environment

Copyright: Copyright (c) 2003-2005

Company: Universita' di Pisa - Dipartimento di Informatica

Author:
Andrea Romei (romei@di.unipi.it)
Version:
2.0.16


Constructor & Destructor Documentation

kddml.Core.DataMining.Clustering.CentroidBasedCluster.CentroidBasedCluster String  name,
double[]  centroid
 

Constructor given the cluster name and the centroid coordinate.

Parameters:
name String
centroid double[] the center coordinate in respect on the ClusteringDescription fields.


Member Function Documentation

boolean kddml.Core.DataMining.Clustering.CentroidBasedCluster.isDistributionBased  ) 
 

Returns true if the clustering is distribution-based.

Returns:
boolean false

Implements kddml.Core.DataMining.Clustering.ClusterManager.

boolean kddml.Core.DataMining.Clustering.CentroidBasedCluster.isCentroidBased  ) 
 

Returns true if the clustering is center-based.

Returns:
boolean true

Implements kddml.Core.DataMining.Clustering.ClusterManager.

Object kddml.Core.DataMining.Clustering.CentroidBasedCluster.getSeedAsInstance  )  throws ClusteringModelException [virtual]
 

Returns the representative cluster seed as instance. Returns the centroid as instance.

Exceptions:
ClusteringModelException 
Returns:
Object a single instance as in weka.core.Instance.

Implements kddml.Core.DataMining.Clustering.ClusterManager.

double [] kddml.Core.DataMining.Clustering.CentroidBasedCluster.getSeedCoordinate  )  [virtual]
 

Returns the centroid coordinate as array of normalized doubles in respect on the ClusterDescription fields.

Exceptions:
ClusteringModelException 
Returns:
double[]

Implements kddml.Core.DataMining.Clustering.ClusterManager.

String kddml.Core.DataMining.Clustering.CentroidBasedCluster.toString  ) 
 

return null;

Returns a representation of this cluster as string.

Returns:
String

Reimplemented from kddml.Core.DataMining.Clustering.Cluster.

Element kddml.Core.DataMining.Clustering.CentroidBasedCluster.toXML  )  [protected]
 

Returns a representation of this cluster as PMML element.

Returns:
Element

Reimplemented from kddml.Core.DataMining.Clustering.Cluster.


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