Kmeans Class Reference

K-Means clustering algorithm. Given a set of observations, k-means partitions the observations into k sets so as to minimize the within-cluster sum of squares. Description based on http://en.wikipedia.org/wiki/K-means_clustering. More...

#include <math/gzmath.hh>

Public Member Functions
	Kmeans (const std::vector< Vector3d > &_obs)
	constructor
bool	AppendObservations (const std::vector< Vector3d > &_obs)
	Add observations to the cluster.
bool	Cluster (int _k, std::vector< Vector3d > &_centroids, std::vector< unsigned int > &_labels)
	Executes the k-means algorithm.
std::vector< Vector3d >	Observations () const
	Get the observations to cluster.
bool	Observations (const std::vector< Vector3d > &_obs)
	Set the observations to cluster.

Detailed Description

K-Means clustering algorithm. Given a set of observations, k-means partitions the observations into k sets so as to minimize the within-cluster sum of squares. Description based on http://en.wikipedia.org/wiki/K-means_clustering.

Constructor & Destructor Documentation

Kmeans()

Kmeans ( const std::vector< Vector3d > &  _obs )

explicit

constructor

Parameters

[in]

_obs

Set of observations to cluster.

Member Function Documentation

AppendObservations()

bool AppendObservations

(

const std::vector< Vector3d > &

_obs

)

Add observations to the cluster.

Parameters

[in]

_obs

Vector of observations.

Returns

True if the _obs vector is not empty or false otherwise.

Cluster()

bool Cluster	(	int	_k,
		std::vector< Vector3d > &	_centroids,
		std::vector< unsigned int > &	_labels
	)

Executes the k-means algorithm.

Parameters

[in]	_k	Number of partitions to cluster.
[out]	_centroids	Vector of centroids. Each element contains the centroid of one cluster.
[out]	_labels	Vector of labels. The size of this vector is equals to the number of observations. Each element represents the cluster to which observation belongs.

Returns

True when the operation succeed or false otherwise. The operation will fail if the number of observations is not positive, if the number of clusters is non positive, or if the number of clusters if greater than the number of observations.

Observations() [1/2]

std::vector< Vector3d > Observations

(

)

const

Get the observations to cluster.

Returns

The vector of observations.

Observations() [2/2]

bool Observations

(

const std::vector< Vector3d > &

_obs

)

Set the observations to cluster.

Parameters

[in]

_obs

The new vector of observations.

Returns

True if the vector is not empty or false otherwise.

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

• Kmeans.hh