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KMeans

Namespace: MATHLIB  ·  Library: Math Library

#include <math/kmeans.h>

class UTILSLIB::KMeans

Lloyd-style K-means partitional clustering with configurable distance metric, seeding strategy, replicate count, empty-cluster policy and optional online refinement phase.

Lloyd-style K-means clustering with configurable metric, seeding and replicates.

Public Methods​

KMeans(distance, start, replicates, emptyact, online, maxit)​

Constructs a KMeans algorithm object.

Parameters:

  • distance : QString (optional) K-Means distance measure: "sqeuclidean" (default), "cityblock" , "cosine", "correlation", "hamming".

  • start : QString (optional) Cluster initialization: "sample" (default), "uniform", "cluster".

  • replicates : qint32 (optional) Number of K-Means replicates, which are generated. Best is returned.

  • emptyact : QString (optional) What happens if a cluster goes empty: "error" (default), "drop", "singleton".

  • online : bool (optional) If centroids should be updated during iterations: true (default), false.

  • maxit : qint32 (optional) Maximal number of iterations per replicate; 100 by default.

KMeans(distance, start, replicates, emptyact, online, maxit)​

Constructs a KMeans algorithm object using enum parameters (preferred for performance).

Parameters:

  • distance : KMeansDistance Distance metric.

  • start : KMeansStart Cluster initialization strategy.

  • replicates : qint32 Number of K-Means replicates. Best is returned.

  • emptyact : KMeansEmptyAction What happens if a cluster goes empty.

  • online : bool If centroids should be updated during iterations.

  • maxit : qint32 Maximal number of iterations per replicate.

calculate(X, kClusters, idx, C, sumD, D)​

Clusters input data X.

Parameters:

  • X : const Eigen::MatrixXd & Input data (rows = points; cols = p dimensional space).

  • kClusters : qint32 Number of k clusters.

  • idx : Eigen::VectorXi & The cluster indices to which cluster the input points belong to.

  • C : Eigen::MatrixXd & Cluster centroids k x p.

  • sumD : Eigen::VectorXd & Summation of the distances to the centroid within one cluster.

  • D : Eigen::MatrixXd & Cluster distances to the centroid.

Returns:

  • bool — true if clustering succeeded, false otherwise.

Authors of this file​