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Java example source code file (MultiKMeansPlusPlusClusterer.java)

This example Java source code file (MultiKMeansPlusPlusClusterer.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

clusterable, clusterevaluator, collection, convergenceexception, kmeansplusplusclusterer, list, mathillegalargumentexception, multikmeansplusplusclusterer, override, util

The MultiKMeansPlusPlusClusterer.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * 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
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

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.
 * @param <T> type of the points to cluster
 * @since 3.2
 */
public class MultiKMeansPlusPlusClusterer<T extends Clusterable> extends Clusterer {

    /** The underlying k-means clusterer. */
    private final KMeansPlusPlusClusterer<T> clusterer;

    /** The number of trial runs. */
    private final int numTrials;

    /** The cluster evaluator to use. */
    private final ClusterEvaluator<T> evaluator;

    /** 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.
     * @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.
     * @return the embedded clusterer
     */
    public KMeansPlusPlusClusterer<T> getClusterer() {
        return clusterer;
    }

    /**
     * Returns the number of trials this instance will do.
     * @return the number of trials
     */
    public int getNumTrials() {
        return numTrials;
    }

    /**
     * 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.
     *
     * @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 cluster(final Collection points)
        throws MathIllegalArgumentException, ConvergenceException {

        // at first, we have not found any clusters list yet
        List<CentroidCluster best = null;
        double bestVarianceSum = Double.POSITIVE_INFINITY;

        // do several clustering trials
        for (int i = 0; i < numTrials; ++i) {

            // compute a clusters list
            List<CentroidCluster 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;

    }

}

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