lti::clustering Class Reference

Base class for all clustering algorithms. More...

#include <ltiClustering.h>

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Collaboration diagram for lti::clustering:

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List of all members.

Classes
class	parameters
	parameters for clustering functors. More...
Public Member Functions
	clustering ()
	clustering (const clustering &other)
virtual	~clustering ()
virtual const char *	getTypeName () const
clustering &	copy (const clustering &other)
const parameters &	getParameters () const
virtual bool	train (const dmatrix &input)=0
virtual bool	train (const dmatrix &input, ivector &ids)

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Detailed Description

Base class for all clustering algorithms.

Clustering algorithms can follow different training strategies as indicated by the parameter clusterMode. Representations of the clusters are modelled in subclasses of this class, e.g. centroidClustering.

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Constructor & Destructor Documentation

lti::clustering::clustering

(

)

default constructor

lti::clustering::clustering

(

const clustering &

other

)

copy constructor

Parameters:

other

the object to be copied

virtual lti::clustering::~clustering

(

)

[virtual]

destructor

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Member Function Documentation

clustering& lti::clustering::copy

(

const clustering &

other

)

copy data of "other" functor.

Parameters:

other

the functor to be copied

Returns:

a reference to this functor object

Reimplemented from lti::unsupervisedClassifier.

Reimplemented in lti::adaptiveKMeans, lti::centroidClustering, lti::competitiveAgglomeration, lti::DBScan< T >, lti::fuzzyCMeans, lti::kMeansClustering, and lti::MSTClustering< U >.

const parameters& lti::clustering::getParameters

(

)

const

returns current parameters.

Reimplemented from lti::unsupervisedClassifier.

Reimplemented in lti::adaptiveKMeans, lti::centroidClustering, lti::competitiveAgglomeration, lti::DBScan< T >, lti::fuzzyCMeans, lti::kMeansClustering, and lti::MSTClustering< U >.

virtual const char* lti::clustering::getTypeName

(

)

const [virtual]

returns the name of this type ("clustering")

Reimplemented from lti::unsupervisedClassifier.

Reimplemented in lti::adaptiveKMeans, lti::centroidClustering, lti::competitiveAgglomeration, lti::DBScan< T >, lti::fuzzyCMeans, lti::kMeansClustering, and lti::MSTClustering< U >.

virtual bool lti::clustering::train	(	const dmatrix &	input,
		ivector &	ids
	)			[virtual]

train the clusterer with the vectors at the rows of input, and return the cluster id for each of that vectors.

Parameters:

	input	the input data
	ids	output vector where the cluster id per input vector will be stored.

Returns:

true if successful, false otherwise.

Reimplemented from lti::unsupervisedClassifier.

Reimplemented in lti::adaptiveKMeans, lti::centroidClustering, lti::competitiveAgglomeration, lti::DBScan< T >, lti::fuzzyCMeans, lti::kMeansClustering, and lti::MSTClustering< U >.

virtual bool lti::clustering::train

(

const dmatrix &

input

)

[pure virtual]

train the clusterer with the vectors at the rows of input

Parameters:

input

the input data

Returns:

true if successful, false otherwise.

Implements lti::unsupervisedClassifier.

Implemented in lti::adaptiveKMeans, lti::centroidClustering, lti::competitiveAgglomeration, lti::DBScan< T >, lti::fuzzyCMeans, lti::kMeansClustering, and lti::MSTClustering< U >.

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

• ltiClustering.h