kmeans::InitializeKmeanspp< Matrix_, Cluster_, Float_ > Class Template Reference

k-means++ initialization of Arthur and Vassilvitskii (2007). More...

#include <InitializeKmeanspp.hpp>

Inheritance diagram for kmeans::InitializeKmeanspp< Matrix_, Cluster_, Float_ >:

Collaboration diagram for kmeans::InitializeKmeanspp< Matrix_, Cluster_, Float_ >:

Public Member Functions
	InitializeKmeanspp (InitializeKmeansppOptions options)
	InitializeKmeanspp ()=default
InitializeKmeansppOptions &	get_options ()
Cluster_	run (const Matrix_ &matrix, Cluster_ ncenters, Float_ *centers) const
Public Member Functions inherited from kmeans::Initialize< Matrix_, Cluster_, Float_ >

Detailed Description

template<typename Matrix_ = SimpleMatrix<double, int>, typename Cluster_ = int, typename Float_ = double>
class kmeans::InitializeKmeanspp< Matrix_, Cluster_, Float_ >

k-means++ initialization of Arthur and Vassilvitskii (2007).

This approach involves the selection of starting points via iterations of weighted sampling, where the sampling probability for each point is proportional to the squared distance to the closest starting point that was chosen in any of the previous iterations. The aim is to obtain well-separated starting points to encourage the formation of suitable clusters.

Template Parameters

Matrix_	Matrix type for the input data. This should satisfy the MockMatrix contract.
Cluster_	Integer type for the cluster assignments.
Float_	Floating-point type for the centroids.

See also

Arthur, D. and Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035.

Constructor & Destructor Documentation

InitializeKmeanspp() [1/2]

template<typename Matrix_ = SimpleMatrix<double, int>, typename Cluster_ = int, typename Float_ = double>

kmeans::InitializeKmeanspp< Matrix_, Cluster_, Float_ >::InitializeKmeanspp ( InitializeKmeansppOptions options )

inline

Parameters

options

Options for kmeans++ initialization.

InitializeKmeanspp() [2/2]

template<typename Matrix_ = SimpleMatrix<double, int>, typename Cluster_ = int, typename Float_ = double>

kmeans::InitializeKmeanspp< Matrix_, Cluster_, Float_ >::InitializeKmeanspp ( )

default

Default constructor.

Member Function Documentation

get_options()

template<typename Matrix_ = SimpleMatrix<double, int>, typename Cluster_ = int, typename Float_ = double>

InitializeKmeansppOptions & kmeans::InitializeKmeanspp< Matrix_, Cluster_, Float_ >::get_options ( )

inline

Returns

Options for kmeans++ partitioning, to be modified prior to calling run().

run()

template<typename Matrix_ = SimpleMatrix<double, int>, typename Cluster_ = int, typename Float_ = double>

Cluster_ kmeans::InitializeKmeanspp< Matrix_, Cluster_, Float_ >::run ( const Matrix_ & data, Cluster_ num_centers, Float_ * centers ) const

inlinevirtual

Parameters

	data	A matrix-like object (see MockMatrix) containing per-observation data.
	num_centers	Number of cluster centers.
[out]	centers	Pointer to an array of length equal to the product of num_centers and data.num_dimensions(). This contains a column-major matrix where rows correspond to dimensions and columns correspond to cluster centers. On output, each column will contain the final centroid locations for each cluster.

Returns

centers is filled with the new cluster centers. The number of filled centers is returned - this is usually equal to num_centers, but may not be if, e.g., num_centers is greater than the number of observations. If the returned value is less than num_centers, only the first few centers in centers will be filled.

Implements kmeans::Initialize< Matrix_, Cluster_, Float_ >.

---

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

• kmeans/InitializeKmeanspp.hpp