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kddml.Core.DataMining.Clustering.DiscreteClusteringField Class Reference

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

kddml.Core.DataMining.Clustering.ClusteringField kddml.Core.DataMining.Clustering.ClusteringFieldManager List of all members.

Public Member Functions

 DiscreteClusteringField (String name, AttributeComparisonMeasure func, String categories[])
String[] getCategories ()
int getNumberOfCategories ()
boolean isContinuous ()
double[] getDistribution (String category) throws ClusteringModelException
String toString ()

Detailed Description

This class represents a discrete centroid field.

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.DiscreteClusteringField.DiscreteClusteringField String  name,
AttributeComparisonMeasure  func,
String  categories[]
 

Constructor.

Parameters:
name String the field name as in MiningSchema.
func AttributeComparisonMeasure the attribute comparison measure. Can be null, for example when a distribution-based function is used.
categories String[] the list of categories for this field.


Member Function Documentation

String [] kddml.Core.DataMining.Clustering.DiscreteClusteringField.getCategories  ) 
 

Returns the categories of this clustering field.

Returns:
String[]

int kddml.Core.DataMining.Clustering.DiscreteClusteringField.getNumberOfCategories  ) 
 

Returns the number of categories of this clustering field.

Returns:
int

boolean kddml.Core.DataMining.Clustering.DiscreteClusteringField.isContinuous  )  [virtual]
 

Returns true if the field is continuous. Returns false if it is discrete.

Returns:
boolean false.

Implements kddml.Core.DataMining.Clustering.ClusteringFieldManager.

double [] kddml.Core.DataMining.Clustering.DiscreteClusteringField.getDistribution String  category  )  throws ClusteringModelException
 

Returns the distribution probability for a given category. In particular, an element (f, v) defines that a category has value 1.0 if the value of input field category is v, otherwise it is 0.

Parameters:
category String
Exceptions:
ClusteringModelException if the category do not exist.
Returns:
double[] a probability vector.

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

Returns a representation of this object as string.

Returns:
String

Reimplemented from kddml.Core.DataMining.Clustering.ClusteringField.


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