STK::IClusterModelBase Class Reference

Base class for clustering models. More...

#include <STK_IClusterModelBase.h>

Inheritance diagram for STK::IClusterModelBase:

Public Member Functions
virtual	~IClusterModelBase ()
	destructor
int	nbCluster () const
CPointX const &	pk () const
CVectorXi const &	zi () const
Public Member Functions inherited from STK::IStatModelBase
int	nbSample () const
Real	lnNbSample () const
int	nbVariable () const
Real	lnLikelihood () const
Real	likelihood () const
int	nbFreeParameter () const
Real	computeBIC () const
Real	computeAIC () const
Real	computeML () const

Protected Member Functions
	IClusterModelBase (int nbCluster)
	Constructor.
	IClusterModelBase (int nbSample, int nbCluster)
	Constructor.
	IClusterModelBase (IClusterModelBase const &model)
	copy constructor.
void	setNbCluster (int nbCluster)
	set the number of cluster of the model
Protected Member Functions inherited from STK::IStatModelBase
	IStatModelBase ()
	Default constructor.
	IStatModelBase (int nbSample)
	Constructor with specified dimension.
	IStatModelBase (int nbSample, int nbVariable)
	Constructor with specified dimension.
	IStatModelBase (IStatModelBase const &model)
	Copy constructor.
	~IStatModelBase ()
	destructor
void	setNbFreeParameter (int const &nbFreeParameter)
	set the number of free parameters of the model
void	setNbSample (int const &nbSample)
	set the number of samples of the model
void	setNbVariable (int const &nbVariable)
	set the number of variables of the model
void	setLnLikelihood (Real const &lnLikelihood)
	set the log-likelihood of the model
void	initialize (int nbSample, int nbVariable)
	set the dimensions of the parameters of the model

Protected Attributes
int	nbCluster_
	number of cluster.
CPointX	pk_
	The proportions of each mixtures.
CVectorXi	zi_
	The zi class label.

Detailed Description

Base class for clustering models.

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.

Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties.

Definition at line 75 of file STK_IClusterModelBase.h.

Constructor & Destructor Documentation

IClusterModelBase() [1/3]

STK::IClusterModelBase::IClusterModelBase ( int  nbCluster )

protected

Constructor.

Parameters

nbCluster

number of clusters

IClusterModelBase() [2/3]

STK::IClusterModelBase::IClusterModelBase ( int  nbSample, int  nbCluster  )

protected

Constructor.

Parameters

nbCluster,nbSample

number of clusters and samples

IClusterModelBase() [3/3]

STK::IClusterModelBase::IClusterModelBase ( IClusterModelBase const &  model )

protected

copy constructor.

Parameters

model

the model to clone

~IClusterModelBase()

virtual STK::IClusterModelBase::~IClusterModelBase ( )

virtual

destructor

Member Function Documentation

nbCluster()

int STK::IClusterModelBase::nbCluster ( ) const

inline

Returns

the number of cluster

Definition at line 96 of file STK_IClusterModelBase.h.

{ return nbCluster_;}

References nbCluster_.

Referenced by setNbCluster().

pk()

CPointX const & STK::IClusterModelBase::pk ( ) const

inline

Returns

the proportions of each mixtures

Definition at line 98 of file STK_IClusterModelBase.h.

{ return pk_;};

References pk_.

setNbCluster()

void STK::IClusterModelBase::setNbCluster ( int  nbCluster )

inlineprotected

set the number of cluster of the model

Parameters

nbCluster

number of cluster of the model

Definition at line 106 of file STK_IClusterModelBase.h.

{ nbCluster_ = nbCluster;}

References nbCluster(), and nbCluster_.

zi()

CVectorXi const & STK::IClusterModelBase::zi ( ) const

inline

Returns

the zi class label

Definition at line 100 of file STK_IClusterModelBase.h.

{ return zi_;};

References zi_.

Member Data Documentation

nbCluster_

int STK::IClusterModelBase::nbCluster_

protected

number of cluster.

Definition at line 108 of file STK_IClusterModelBase.h.

Referenced by nbCluster(), and setNbCluster().

pk_

CPointX STK::IClusterModelBase::pk_

protected

The proportions of each mixtures.

Definition at line 110 of file STK_IClusterModelBase.h.

Referenced by pk().

zi_

CVectorXi STK::IClusterModelBase::zi_

protected

The zi class label.

Definition at line 112 of file STK_IClusterModelBase.h.

Referenced by zi().

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

• STK_IClusterModelBase.h