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 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
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package org.apache.commons.math3.ml.clustering;

import java.util.Collection;
import java.util.List;

import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.ml.clustering.evaluation.ClusterEvaluator;
import org.apache.commons.math3.ml.clustering.evaluation.SumOfClusterVariances;

A wrapper around a k-means++ clustering algorithm which performs multiple trials and returns the best solution.
Type parameters:
  • <T> – type of the points to cluster
Since:3.2
/** * A wrapper around a k-means++ clustering algorithm which performs multiple trials * and returns the best solution. * @param <T> type of the points to cluster * @since 3.2 */
public class MultiKMeansPlusPlusClusterer<T extends Clusterable> extends Clusterer<T> {
The underlying k-means clusterer.
/** The underlying k-means clusterer. */
private final KMeansPlusPlusClusterer<T> clusterer;
The number of trial runs.
/** The number of trial runs. */
private final int numTrials;
The cluster evaluator to use.
/** The cluster evaluator to use. */
private final ClusterEvaluator<T> evaluator;
Build a clusterer.
Params:
  • clusterer – the k-means clusterer to use
  • numTrials – number of trial runs
/** Build a clusterer. * @param clusterer the k-means clusterer to use * @param numTrials number of trial runs */
public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer, final int numTrials) { this(clusterer, numTrials, new SumOfClusterVariances<T>(clusterer.getDistanceMeasure())); }
Build a clusterer.
Params:
  • clusterer – the k-means clusterer to use
  • numTrials – number of trial runs
  • evaluator – the cluster evaluator to use
Since:3.3
/** Build a clusterer. * @param clusterer the k-means clusterer to use * @param numTrials number of trial runs * @param evaluator the cluster evaluator to use * @since 3.3 */
public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer, final int numTrials, final ClusterEvaluator<T> evaluator) { super(clusterer.getDistanceMeasure()); this.clusterer = clusterer; this.numTrials = numTrials; this.evaluator = evaluator; }
Returns the embedded k-means clusterer used by this instance.
Returns:the embedded clusterer
/** * Returns the embedded k-means clusterer used by this instance. * @return the embedded clusterer */
public KMeansPlusPlusClusterer<T> getClusterer() { return clusterer; }
Returns the number of trials this instance will do.
Returns:the number of trials
/** * Returns the number of trials this instance will do. * @return the number of trials */
public int getNumTrials() { return numTrials; }
Returns the ClusterEvaluator used to determine the "best" clustering.
Returns:the used ClusterEvaluator
Since:3.3
/** * Returns the {@link ClusterEvaluator} used to determine the "best" clustering. * @return the used {@link ClusterEvaluator} * @since 3.3 */
public ClusterEvaluator<T> getClusterEvaluator() { return evaluator; }
Runs the K-means++ clustering algorithm.
Params:
  • points – the points to cluster
Throws:
Returns:a list of clusters containing the points
/** * Runs the K-means++ clustering algorithm. * * @param points the points to cluster * @return a list of clusters containing the points * @throws MathIllegalArgumentException if the data points are null or the number * of clusters is larger than the number of data points * @throws ConvergenceException if an empty cluster is encountered and the * underlying {@link KMeansPlusPlusClusterer} has its * {@link KMeansPlusPlusClusterer.EmptyClusterStrategy} is set to {@code ERROR}. */
@Override public List<CentroidCluster<T>> cluster(final Collection<T> points) throws MathIllegalArgumentException, ConvergenceException { // at first, we have not found any clusters list yet List<CentroidCluster<T>> best = null; double bestVarianceSum = Double.POSITIVE_INFINITY; // do several clustering trials for (int i = 0; i < numTrials; ++i) { // compute a clusters list List<CentroidCluster<T>> clusters = clusterer.cluster(points); // compute the variance of the current list final double varianceSum = evaluator.score(clusters); if (evaluator.isBetterScore(varianceSum, bestVarianceSum)) { // this one is the best we have found so far, remember it best = clusters; bestVarianceSum = varianceSum; } } // return the best clusters list found return best; } }