A simple clustering method to choose k clusters from a set of data. More...

#include <KMeans.h>

Public Member Functions
	KMeans (const std::vector< Vector > &data)

	KMeans (const std::vector< Vector > &data, int k)

int	GetK () const

void	SetK (int k)

void	RandomInitialCenters ()
	Initialization.

void	ClearLabels ()

void	Iterate (int &maxIters)
	Returns in maxIters the number of used iterations before convergence.

bool	CalcLabelsFromCenters ()
	Returns true if any label has changed.

void	CalcCentersFromLabels ()
	Sets the centers from the data points in a center's group.

Real	AverageDistance (int c)
	Returns the average distance of points for the given cluster.

void	AverageDistance (std::vector< Real > &dist)
	Same as above, but for all clusters.

virtual Real	Distance (const Vector &a, const Vector &b)
	Overrideable: distance metric.

Detailed Description

A simple clustering method to choose k clusters from a set of data.

The cluster centers are

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

Public Member Functions