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kddml.Core.DataMining.Clustering.SIMPLE_MATCHING Class Reference

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

kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure kddml.Core.DataMining.Clustering.ComparisonMeasure List of all members.

Public Member Functions

 SIMPLE_MATCHING (AttributeComparisonMeasure[] attribute_comparison, double[] weights)
 SIMPLE_MATCHING (ClusterDescription cluster_description)
boolean isSimilarityMeasure ()
double compare (ClusterManager cluster, Instance instance) throws ClusteringModelException
String getFunctionName ()

Protected Member Functions

double evaluate (double[] x)

Detailed Description

The simple matching function is an outer function, used to compare two binary or categorical records X, Y where: The simple matching function is defined as:

D = ( a11 + a00 ) / ( a11 + a10 + a01 + a00 ).

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.SIMPLE_MATCHING.SIMPLE_MATCHING AttributeComparisonMeasure[]  attribute_comparison,
double[]  weights
 

Constructor given the list of comparison measures. Used for centroid-based clustering only.

Parameters:
attribute_comparison AttributeComparisonMeasure[] the comparison measure for each attribute.
weights double[] the field weight for each attribute. Can be null.

kddml.Core.DataMining.Clustering.SIMPLE_MATCHING.SIMPLE_MATCHING ClusterDescription  cluster_description  ) 
 

Constructor given the centroid description. Used both for centroid-based clustering and distribution-based clustering.

Parameters:
cluster_description ClusterDescription


Member Function Documentation

boolean kddml.Core.DataMining.Clustering.SIMPLE_MATCHING.isSimilarityMeasure  )  [virtual]
 

Returns true if the comparison measure is a similarity function, in which the value returned by the compare method is optimal for greater values. Returns false if the comparison measure is a distance measure, in which the value returned by the compare method is optimal if it is 0.

Returns:
boolean true

Implements kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure.

double kddml.Core.DataMining.Clustering.SIMPLE_MATCHING.compare ClusterManager  cluster,
Instance  instance
throws ClusteringModelException
 

Compares an input istance with the seed of the cluster. The seed depends on the type of clustering. If the clustering is center-based, the seed is the centroid as instance. If the clustering is distribution-based, the seed is calculated on the statistics associated to the cluster. In particular, for numeric attributes, the mean of cluster instances is used as seed. For discrete attribute, the most probable category is reported as seed.

Parameters:
cluster ClusterManager
instance Instance
Exceptions:
ClusteringModelException 
Returns:
double the optimal values depends on the isSimilarityMeasure method.

Reimplemented from kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure.

double kddml.Core.DataMining.Clustering.SIMPLE_MATCHING.evaluate double[]  x  )  [protected, virtual]
 

Evaluates the comparison measure given the comparison values for each single attribute (i.e., the values returned by each single inner function).

Parameters:
x double[]
Returns:
double 0

Implements kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure.

String kddml.Core.DataMining.Clustering.SIMPLE_MATCHING.getFunctionName  )  [virtual]
 

Returns the function name as in PMML.

Returns:
String

Implements kddml.Core.DataMining.Clustering.ComparisonMeasure.


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