lti::clusteringValidity Class Reference

Parent class for all clustering validity measures. More...

#include <ltiClusteringValidity.h>

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List of all members.

Public Member Functions
	clusteringValidity ()
	clusteringValidity (const clusteringValidity &other)
virtual	~clusteringValidity ()
virtual const char *	getTypeName () const
clusteringValidity &	operator= (const clusteringValidity &other)
clusteringValidity &	copy (const clusteringValidity &other)
virtual bool	apply (const std::vector< dmatrix > &clusteredData, double &index, const dmatrix &centroids) const =0
Protected Member Functions
double	getMinimumDistance (const dmatrix &m1, const dmatrix &m2) const
double	getMaximumDistance (const dmatrix &m1, const dmatrix &m2) const
double	getCentroidDistance (const dmatrix &m1, const dmatrix &m2) const
double	getAverageDistance (const dmatrix &m1, const dmatrix &m2) const
double	getAverageInterpointDistance (const dmatrix &m1, const dmatrix &m2) const
double	getStandardDiameter (const dmatrix &m1) const
double	getAverageDiameter (const dmatrix &m1) const
double	getAverageToCentroidDiameter (const dmatrix &m1) const

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Detailed Description

Parent class for all clustering validity measures.

Clustering validity measures are used to evaluate the quality of a clustering. The measure can e.a. be used to find the best possible number of clusters in a data set. It provides some distance and diameter measure for clusters. These measures are descriped in IEEE Transaction on systems,man and cybernetics - part B: cybernetics, Vol 28, No.3, June 1998, 301-315

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Constructor & Destructor Documentation

lti::clusteringValidity::clusteringValidity

(

)

default constructor

lti::clusteringValidity::clusteringValidity

(

const clusteringValidity &

other

)

default constructor

virtual lti::clusteringValidity::~clusteringValidity

(

)

[virtual]

destructor

---

Member Function Documentation

virtual bool lti::clusteringValidity::apply	(	const std::vector< dmatrix > &	clusteredData,
		double &	index,
		const dmatrix &	centroids
	)			const [pure virtual]

abstract parant class operates on the given parameter.

Parameters:

	clusteredData	std::vector<dmatrix> with the source data.
	index	the clustering validity result
	centroids	dmatrix with each row representing a centroid of the corresponding distribution in clusteredData

Returns:

true if apply successful or false otherwise.

Implemented in lti::dunnIndex, lti::modHubertStat, lti::normModHubertStat, and lti::daviesBouldinIndex.

clusteringValidity& lti::clusteringValidity::copy

(

const clusteringValidity &

other

)

copy data of "other" functor.

Parameters:

other

the functor to be copied

Returns:

a reference to this functor object

Reimplemented from lti::functor.

Reimplemented in lti::dunnIndex, lti::modHubertStat, lti::normModHubertStat, and lti::daviesBouldinIndex.

double lti::clusteringValidity::getAverageDiameter

(

const dmatrix &

m1

)

const [protected]

the average distance of all points in m1

double lti::clusteringValidity::getAverageDistance	(	const dmatrix &	m1,
		const dmatrix &	m2
	)			const [protected]

return the average distance of all the point in m1 and m2

double lti::clusteringValidity::getAverageInterpointDistance	(	const dmatrix &	m1,
		const dmatrix &	m2
	)			const [protected]

accumulates the the distances of all points each matrix to matrix and divides the sum of these distance through the sum of all points

double lti::clusteringValidity::getAverageToCentroidDiameter

(

const dmatrix &

m1

)

const [protected]

average distance between each point and the centroid of m1

double lti::clusteringValidity::getCentroidDistance	(	const dmatrix &	m1,
		const dmatrix &	m2
	)			const [protected]

return the distance of the centroids of m1 and m2

double lti::clusteringValidity::getMaximumDistance	(	const dmatrix &	m1,
		const dmatrix &	m2
	)			const [protected]

calculates the maximum distance of all the points in the given matrices and returns this distance

double lti::clusteringValidity::getMinimumDistance	(	const dmatrix &	m1,
		const dmatrix &	m2
	)			const [protected]

calculates the minimum distances of the points in the given matrices and returns it.

double lti::clusteringValidity::getStandardDiameter

(

const dmatrix &

m1

)

const [protected]

return the maximum distance of all points in m1

virtual const char* lti::clusteringValidity::getTypeName

(

)

const [virtual]

returns the name of this type ("clusteringValidity")

Reimplemented from lti::functor.

Reimplemented in lti::dunnIndex, lti::modHubertStat, lti::normModHubertStat, and lti::daviesBouldinIndex.

clusteringValidity& lti::clusteringValidity::operator=

(

const clusteringValidity &

other

)

alias for copy member

Parameters:

other

the functor to be copied

Returns:

a reference to this functor object

Reimplemented from lti::functor.

Reimplemented in lti::dunnIndex, lti::modHubertStat, lti::normModHubertStat, and lti::daviesBouldinIndex.

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The documentation for this class was generated from the following file:

• ltiClusteringValidity.h