This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm. More...

Public Member Functions
	KMeans (const size_t maxIterations=1000, const MetricType metric=MetricType(), const InitialPartitionPolicy partitioner=InitialPartitionPolicy(), const EmptyClusterPolicy emptyClusterAction=EmptyClusterPolicy())
	Create a K-Means object and (optionally) set the parameters which K-Means will be run with. More...

void	Cluster (const MatType &data, const size_t clusters, arma::Row< size_t > &assignments, const bool initialGuess=false)
	Perform k-means clustering on the data, returning a list of cluster assignments. More...

void	Cluster (const MatType &data, size_t clusters, arma::mat &centroids, const bool initialGuess=false)
	Perform k-means clustering on the data, returning the centroids of each cluster in the centroids matrix. More...

void	Cluster (const MatType &data, const size_t clusters, arma::Row< size_t > &assignments, arma::mat &centroids, const bool initialAssignmentGuess=false, const bool initialCentroidGuess=false)
	Perform k-means clustering on the data, returning a list of cluster assignments and also the centroids of each cluster. More...

const EmptyClusterPolicy &	EmptyClusterAction () const
	Get the empty cluster policy. More...

EmptyClusterPolicy &	EmptyClusterAction ()
	Modify the empty cluster policy. More...

size_t	MaxIterations () const
	Get the maximum number of iterations. More...

size_t &	MaxIterations ()
	Set the maximum number of iterations. More...

const MetricType &	Metric () const
	Get the distance metric. More...

MetricType &	Metric ()
	Modify the distance metric. More...

const InitialPartitionPolicy &	Partitioner () const
	Get the initial partitioning policy. More...

InitialPartitionPolicy &	Partitioner ()
	Modify the initial partitioning policy. More...

template < typename Archive >
void	serialize (Archive &ar, const uint32_t version)
	Serialize the k-means object. More...

Detailed Description

template
<
typename
MetricType
=
metric::EuclideanDistance
,
typename
InitialPartitionPolicy
=
SampleInitialization
,
typename
EmptyClusterPolicy
=
MaxVarianceNewCluster
,
template
<
class
,
class
>
class
LloydStepType
=
NaiveKMeans
,
typename
MatType
=
arma::mat
>

class mlpack::kmeans::KMeans< MetricType, InitialPartitionPolicy, EmptyClusterPolicy, LloydStepType, MatType >

This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm.

Four template parameters can (optionally) be supplied: the distance metric to use, the policy for how to find the initial partition of the data, the actions to be taken when an empty cluster is encountered, and the implementation of a single Lloyd step to use.

A simple example of how to run K-Means clustering is shown below.

extern arma::mat data; // Dataset we want to run K-Means on.
arma::Row<size_t> assignments; // Cluster assignments.
arma::mat centroids; // Cluster centroids.
KMeans<> k; // Default options.
k.Cluster(data, 3, assignments, centroids); // 3 clusters.
// Cluster using the Manhattan distance, 100 iterations maximum, saving only
// the centroids.
KMeans<metric::ManhattanDistance> k(100);
k.Cluster(data, 6, centroids); // 6 clusters.

Template Parameters

MetricType	The distance metric to use for this KMeans; see metric::LMetric for an example.
InitialPartitionPolicy	Initial partitioning policy; must implement a default constructor and either 'void Cluster(const arma::mat&, const size_t, arma::Row<size_t>&)' or 'void Cluster(const arma::mat&, const size_t, arma::mat&)'.
EmptyClusterPolicy	Policy for what to do on an empty cluster; must implement a default constructor and 'void EmptyCluster(const arma::mat& data, const size_t emptyCluster, const arma::mat& oldCentroids, arma::mat& newCentroids, arma::Col<size_t>& counts, MetricType& metric, const size_t iteration)'.
LloydStepType	Implementation of single Lloyd step to use.

See also: RandomPartition, SampleInitialization, RefinedStart, AllowEmptyClusters, MaxVarianceNewCluster, NaiveKMeans, ElkanKMeans

Definition at line 73 of file kmeans.hpp.

Constructor & Destructor Documentation

◆ KMeans()

KMeans	(	const size_t	maxIterations = `1000`,
		const MetricType	metric = `MetricType()`,
		const InitialPartitionPolicy	partitioner = `InitialPartitionPolicy()`,
		const EmptyClusterPolicy	emptyClusterAction = `EmptyClusterPolicy()`
	)

Create a K-Means object and (optionally) set the parameters which K-Means will be run with.

Parameters

maxIterations	Maximum number of iterations allowed before giving up (0 is valid, but the algorithm may never terminate).
metric	Optional MetricType object; for when the metric has state it needs to store.
partitioner	Optional InitialPartitionPolicy object; for when a specially initialized partitioning policy is required.
emptyClusterAction	Optional EmptyClusterPolicy object; for when a specially initialized empty cluster policy is required.

Member Function Documentation

◆ Cluster() [1/3]

void Cluster	(	const MatType &	data,
		const size_t	clusters,
		arma::Row< size_t > &	assignments,
		const bool	initialGuess = `false`
	)

Perform k-means clustering on the data, returning a list of cluster assignments.

Optionally, the vector of assignments can be set to an initial guess of the cluster assignments; to do this, set initialGuess to true.

Template Parameters

MatType Type of matrix (arma::mat or arma::sp_mat).

Parameters

data	Dataset to cluster.
clusters	Number of clusters to compute.
assignments	Vector to store cluster assignments in.
initialGuess	If true, then it is assumed that assignments has a list of initial cluster assignments.

◆ Cluster() [2/3]

void Cluster	(	const MatType &	data,
		size_t	clusters,
		arma::mat &	centroids,
		const bool	initialGuess = `false`
	)

Perform k-means clustering on the data, returning the centroids of each cluster in the centroids matrix.

Optionally, the initial centroids can be specified by filling the centroids matrix with the initial centroids and specifying initialGuess = true.

Template Parameters

MatType Type of matrix (arma::mat or arma::sp_mat).

Parameters

data	Dataset to cluster.
clusters	Number of clusters to compute.
centroids	Matrix in which centroids are stored.
initialGuess	If true, then it is assumed that centroids contains the initial cluster centroids.

◆ Cluster() [3/3]

void Cluster	(	const MatType &	data,
		const size_t	clusters,
		arma::Row< size_t > &	assignments,
		arma::mat &	centroids,
		const bool	initialAssignmentGuess = `false`,
		const bool	initialCentroidGuess = `false`
	)

Perform k-means clustering on the data, returning a list of cluster assignments and also the centroids of each cluster.

Optionally, the vector of assignments can be set to an initial guess of the cluster assignments; to do this, set initialAssignmentGuess to true. Another way to set initial cluster guesses is to fill the centroids matrix with the centroid guesses, and then set initialCentroidGuess to true. initialAssignmentGuess supersedes initialCentroidGuess, so if both are set to true, the assignments vector is used.

Template Parameters

MatType Type of matrix (arma::mat or arma::sp_mat).

Parameters

data	Dataset to cluster.
clusters	Number of clusters to compute.
assignments	Vector to store cluster assignments in.
centroids	Matrix in which centroids are stored.
initialAssignmentGuess	If true, then it is assumed that assignments has a list of initial cluster assignments.
initialCentroidGuess	If true, then it is assumed that centroids contains the initial centroids of each cluster.

◆ EmptyClusterAction() [1/2]

const EmptyClusterPolicy& EmptyClusterAction ( ) const

inline

Get the empty cluster policy.

Definition at line 174 of file kmeans.hpp.

◆ EmptyClusterAction() [2/2]

EmptyClusterPolicy& EmptyClusterAction ( )

inline

Modify the empty cluster policy.

Definition at line 177 of file kmeans.hpp.

References KMeans< MetricType, InitialPartitionPolicy, EmptyClusterPolicy, LloydStepType, MatType >::serialize().

◆ MaxIterations() [1/2]

size_t MaxIterations ( ) const

inline

Get the maximum number of iterations.

Definition at line 159 of file kmeans.hpp.

◆ MaxIterations() [2/2]

size_t& MaxIterations ( )

inline

Set the maximum number of iterations.

Definition at line 161 of file kmeans.hpp.

◆ Metric() [1/2]

const MetricType& Metric ( ) const

inline

Get the distance metric.

Definition at line 164 of file kmeans.hpp.

◆ Metric() [2/2]

MetricType& Metric ( )

inline

Modify the distance metric.

Definition at line 166 of file kmeans.hpp.

◆ Partitioner() [1/2]

const InitialPartitionPolicy& Partitioner ( ) const

inline

Get the initial partitioning policy.

Definition at line 169 of file kmeans.hpp.

◆ Partitioner() [2/2]

InitialPartitionPolicy& Partitioner ( )

inline

Modify the initial partitioning policy.

Definition at line 171 of file kmeans.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t	version
	)

Serialize the k-means object.

Referenced by KMeans< MetricType, InitialPartitionPolicy, EmptyClusterPolicy, LloydStepType, MatType >::EmptyClusterAction().

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/kmeans/kmeans.hpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ KMeans()

Member Function Documentation

◆ Cluster() [1/3]

◆ Cluster() [2/3]

◆ Cluster() [3/3]

◆ EmptyClusterAction() [1/2]

◆ EmptyClusterAction() [2/2]

◆ MaxIterations() [1/2]

◆ MaxIterations() [2/2]

◆ Metric() [1/2]

◆ Metric() [2/2]

◆ Partitioner() [1/2]

◆ Partitioner() [2/2]

◆ serialize()