Main Page | Class Hierarchy | Class List | Class Members

kddml.Core.DataMining.Clustering.MINKOWSKI Class Reference

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

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

Public Member Functions

 MINKOWSKI (AttributeComparisonMeasure[] attribute_comparison, double[] weights, int p_param)
 MINKOWSKI (ClusterDescription cluster_description, int p_param)
boolean isSimilarityMeasure ()
String getFunctionName ()

Protected Member Functions

double evaluate (double[] x)

Detailed Description

The minkowcki function is an outer function, used to compare two records X, Y, where: The minkowski distance is defined as:

D = (sum |Xi-Yi|^p)^(1/p).

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.MINKOWSKI.MINKOWSKI AttributeComparisonMeasure[]  attribute_comparison,
double[]  weights,
int  p_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.
p_param int the positive p-parameter.

kddml.Core.DataMining.Clustering.MINKOWSKI.MINKOWSKI ClusterDescription  cluster_description,
int  p_param
 

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

Parameters:
cluster_description ClusterDescription
p_param int the positive p-parameter.


Member Function Documentation

boolean kddml.Core.DataMining.Clustering.MINKOWSKI.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 false

Implements kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure.

double kddml.Core.DataMining.Clustering.MINKOWSKI.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

Implements kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure.

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

Returns the function name as in PMML.

Returns:
String

Implements kddml.Core.DataMining.Clustering.ComparisonMeasure.


Generated on Thu Feb 23 13:04:41 2006 for kddml by  doxygen 1.4.3