kmeans::Refine< Index_, Data_, Cluster_, Float_, Matrix_ > Class Template Reference

Interface for k-means refinement algorithms. More...

#include <Refine.hpp>

Inheritance diagram for kmeans::Refine< Index_, Data_, Cluster_, Float_, Matrix_ >:

Public Member Functions
virtual Details< Index_ >	run (const Matrix_ &data, Cluster_ num_centers, Float_ *centers, Cluster_ *clusters) const =0

Detailed Description

template<typename Index_, typename Data_, typename Cluster_, typename Float_, typename Matrix_ = Matrix<Index_, Data_>>
class kmeans::Refine< Index_, Data_, Cluster_, Float_, Matrix_ >

Interface for k-means refinement algorithms.

Template Parameters

Index_	Integer type of the observation indices. This should be the same as the index type of Matrix_.
Data_	Numeric type of the input dataset. This should be the same as the data type of Matrix_.
Cluster_	Integer type of the cluster assignments.
Float_	Floating-point type of the centroids.
Matrix_	Class satisfying the Matrix interface.

Member Function Documentation

run()

template<typename Index_ , typename Data_ , typename Cluster_ , typename Float_ , typename Matrix_ = Matrix<Index_, Data_>>

virtual Details< Index_ > kmeans::Refine< Index_, Data_, Cluster_, Float_, Matrix_ >::run ( const Matrix_ & data, Cluster_ num_centers, Float_ * centers, Cluster_ * clusters ) const

pure virtual

Parameters

	data	A matrix containing data for each observation.
	num_centers	Number of cluster centers.
[in,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 input, column j should contain the initial centroid location for cluster j. On output, each column will contain the final centroid location for its corresponding cluster.
[out]	clusters	Pointer to an array of length equal to the number of observations (from data.num_observations()). On output, this will contain the 0-based cluster assignment for each observation. Specifically, each entry is an index that refers to a column of centers and is no greater than num_centers.

Returns

centers and clusters are filled, and an object is returned containing clustering statistics.

Not all columns of centers may be represented in the output clusters, i.e., some clusters may be unused. The remove_unused_centers() function will rearrange the cluster assignments to more easily skip these empty clusters. In practice, empty clusters should be rare if the initial centroids are chosen appropriately.

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

• kmeans/Refine.hpp