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kddml.Core.DataMining.Clustering.BINARY_SIMILARITY Class Reference

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

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

Public Member Functions

 BINARY_SIMILARITY (AttributeComparisonMeasure[] attribute_comparison, double[] weights, double c00_param, double c01_param, double c10_param, double c11_param, double d00_param, double d01_param, double d10_param, double d11_param)
 BINARY_SIMILARITY (ClusterDescription cluster_description, double c00_param, double c01_param, double c10_param, double c11_param, double d00_param, double d01_param, double d10_param, double d11_param) throws ClusteringModelException
boolean isSimilarityMeasure ()
String getFunctionName ()

Protected Member Functions

double evaluate (double[] x)

Detailed Description

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

    c11*a11 + c10*a10 + c01*a01 + c00*a00
D = --------------------------------------------------------
    d11*a11 + d10*a10 + d01*a01 + d00*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.BINARY_SIMILARITY.BINARY_SIMILARITY AttributeComparisonMeasure[]  attribute_comparison,
double[]  weights,
double  c00_param,
double  c01_param,
double  c10_param,
double  c11_param,
double  d00_param,
double  d01_param,
double  d10_param,
double  d11_param
 

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.
c00_param double
c01_param double
c10_param double
c11_param double
d00_param double
d01_param double
d10_param double
d11_param double

kddml.Core.DataMining.Clustering.BINARY_SIMILARITY.BINARY_SIMILARITY ClusterDescription  cluster_description,
double  c00_param,
double  c01_param,
double  c10_param,
double  c11_param,
double  d00_param,
double  d01_param,
double  d10_param,
double  d11_param
throws ClusteringModelException
 

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

Parameters:
cluster_description ClusterDescription
c00_param double
c01_param double
c10_param double
c11_param double
d00_param double
d01_param double
d10_param double
d11_param double
Exceptions:
ClusteringModelException 


Member Function Documentation

boolean kddml.Core.DataMining.Clustering.BINARY_SIMILARITY.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.BINARY_SIMILARITY.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.BINARY_SIMILARITY.getFunctionName  )  [virtual]
 

Returns the function name as in PMML.

Returns:
String

Implements kddml.Core.DataMining.Clustering.ComparisonMeasure.


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