Java Code Examples for org.apache.commons.math3.exception.util.LocalizedFormats#EMPTY_CLUSTER_IN_K_MEANS

The following examples show how to use org.apache.commons.math3.exception.util.LocalizedFormats#EMPTY_CLUSTER_IN_K_MEANS . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest number of points
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 */
private T getPointFromLargestNumberCluster(final Collection<Cluster<T>> clusters) {

    int maxNumber = 0;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {

        // get the number of points of the current cluster
        final int number = cluster.getPoints().size();

        // select the cluster with the largest number of points
        if (number > maxNumber) {
            maxNumber = number;
            selected = cluster;
        }

    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 2
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestVarianceCluster(final Collection<CentroidCluster<T>> clusters)
        throws ConvergenceException {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final CentroidCluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final Clusterable center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(distance(point, center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 3
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 */
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters) {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final T center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(point.distanceFrom(center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 4
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get the point farthest to its cluster center
 *
 * @param clusters the {@link Cluster}s to search
 * @return point farthest to its cluster center
 * @throws ConvergenceException if clusters are all empty
 */
private T getFarthestPoint(final Collection<Cluster<T>> clusters) throws ConvergenceException {

    double maxDistance = Double.NEGATIVE_INFINITY;
    Cluster<T> selectedCluster = null;
    int selectedPoint = -1;
    for (final Cluster<T> cluster : clusters) {

        // get the farthest point
        final T center = cluster.getCenter();
        final List<T> points = cluster.getPoints();
        for (int i = 0; i < points.size(); ++i) {
            final double distance = points.get(i).distanceFrom(center);
            if (distance > maxDistance) {
                maxDistance     = distance;
                selectedCluster = cluster;
                selectedPoint   = i;
            }
        }

    }

    // did we find at least one non-empty cluster ?
    if (selectedCluster == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    return selectedCluster.getPoints().remove(selectedPoint);

}
Example 5
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get the point farthest to its cluster center
 *
 * @param clusters the {@link Cluster}s to search
 * @return point farthest to its cluster center
 */
private T getFarthestPoint(final Collection<Cluster<T>> clusters) {

    double maxDistance = Double.NEGATIVE_INFINITY;
    Cluster<T> selectedCluster = null;
    int selectedPoint = -1;
    for (final Cluster<T> cluster : clusters) {

        // get the farthest point
        final T center = cluster.getCenter();
        final List<T> points = cluster.getPoints();
        for (int i = 0; i < points.size(); ++i) {
            final double distance = points.get(i).distanceFrom(center);
            if (distance > maxDistance) {
                maxDistance     = distance;
                selectedCluster = cluster;
                selectedPoint   = i;
            }
        }

    }

    // did we find at least one non-empty cluster ?
    if (selectedCluster == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    return selectedCluster.getPoints().remove(selectedPoint);

}
Example 6
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest number of points
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestNumberCluster(final Collection<Cluster<T>> clusters) throws ConvergenceException {

    int maxNumber = 0;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {

        // get the number of points of the current cluster
        final int number = cluster.getPoints().size();

        // select the cluster with the largest number of points
        if (number > maxNumber) {
            maxNumber = number;
            selected = cluster;
        }

    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 7
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters)
throws ConvergenceException {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final T center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(point.distanceFrom(center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 8
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest number of points
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestNumberCluster(final Collection<? extends Cluster<T>> clusters)
        throws ConvergenceException {

    int maxNumber = 0;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {

        // get the number of points of the current cluster
        final int number = cluster.getPoints().size();

        // select the cluster with the largest number of points
        if (number > maxNumber) {
            maxNumber = number;
            selected = cluster;
        }

    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 9
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestVarianceCluster(final Collection<CentroidCluster<T>> clusters)
        throws ConvergenceException {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final CentroidCluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final Clusterable center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(distance(point, center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 10
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get the point farthest to its cluster center
 *
 * @param clusters the {@link Cluster}s to search
 * @return point farthest to its cluster center
 */
private T getFarthestPoint(final Collection<Cluster<T>> clusters) {

    double maxDistance = Double.NEGATIVE_INFINITY;
    Cluster<T> selectedCluster = null;
    int selectedPoint = -1;
    for (final Cluster<T> cluster : clusters) {

        // get the farthest point
        final T center = cluster.getCenter();
        final List<T> points = cluster.getPoints();
        for (int i = 0; i < points.size(); ++i) {
            final double distance = points.get(i).distanceFrom(center);
            if (distance > maxDistance) {
                maxDistance     = distance;
                selectedCluster = cluster;
                selectedPoint   = i;
            }
        }

    }

    // did we find at least one non-empty cluster ?
    if (selectedCluster == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    return selectedCluster.getPoints().remove(selectedPoint);

}
Example 11
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestVarianceCluster(final Collection<CentroidCluster<T>> clusters)
        throws ConvergenceException {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final CentroidCluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final Clusterable center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(distance(point, center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 12
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 */
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters) {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final T center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(point.distanceFrom(center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 13
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get the point farthest to its cluster center
 *
 * @param clusters the {@link Cluster}s to search
 * @return point farthest to its cluster center
 * @throws ConvergenceException if clusters are all empty
 */
private T getFarthestPoint(final Collection<CentroidCluster<T>> clusters) throws ConvergenceException {

    double maxDistance = Double.NEGATIVE_INFINITY;
    Cluster<T> selectedCluster = null;
    int selectedPoint = -1;
    for (final CentroidCluster<T> cluster : clusters) {

        // get the farthest point
        final Clusterable center = cluster.getCenter();
        final List<T> points = cluster.getPoints();
        for (int i = 0; i < points.size(); ++i) {
            final double distance = distance(points.get(i), center);
            if (distance > maxDistance) {
                maxDistance     = distance;
                selectedCluster = cluster;
                selectedPoint   = i;
            }
        }

    }

    // did we find at least one non-empty cluster ?
    if (selectedCluster == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    return selectedCluster.getPoints().remove(selectedPoint);

}
Example 14
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters)
throws ConvergenceException {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final T center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(point.distanceFrom(center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 15
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 */
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters) {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final T center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(point.distanceFrom(center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 16
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get the point farthest to its cluster center
 *
 * @param clusters the {@link Cluster}s to search
 * @return point farthest to its cluster center
 * @throws ConvergenceException if clusters are all empty
 */
private T getFarthestPoint(final Collection<Cluster<T>> clusters) throws ConvergenceException {

    double maxDistance = Double.NEGATIVE_INFINITY;
    Cluster<T> selectedCluster = null;
    int selectedPoint = -1;
    for (final Cluster<T> cluster : clusters) {

        // get the farthest point
        final T center = cluster.getCenter();
        final List<T> points = cluster.getPoints();
        for (int i = 0; i < points.size(); ++i) {
            final double distance = points.get(i).distanceFrom(center);
            if (distance > maxDistance) {
                maxDistance     = distance;
                selectedCluster = cluster;
                selectedPoint   = i;
            }
        }

    }

    // did we find at least one non-empty cluster ?
    if (selectedCluster == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    return selectedCluster.getPoints().remove(selectedPoint);

}
Example 17
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters)
throws ConvergenceException {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final T center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(point.distanceFrom(center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 18
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest number of points
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestNumberCluster(final Collection<Cluster<T>> clusters) throws ConvergenceException {

    int maxNumber = 0;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {

        // get the number of points of the current cluster
        final int number = cluster.getPoints().size();

        // select the cluster with the largest number of points
        if (number > maxNumber) {
            maxNumber = number;
            selected = cluster;
        }

    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example 19
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get the point farthest to its cluster center
 *
 * @param clusters the {@link Cluster}s to search
 * @return point farthest to its cluster center
 * @throws ConvergenceException if clusters are all empty
 */
private T getFarthestPoint(final Collection<CentroidCluster<T>> clusters) throws ConvergenceException {

    double maxDistance = Double.NEGATIVE_INFINITY;
    Cluster<T> selectedCluster = null;
    int selectedPoint = -1;
    for (final CentroidCluster<T> cluster : clusters) {

        // get the farthest point
        final Clusterable center = cluster.getCenter();
        final List<T> points = cluster.getPoints();
        for (int i = 0; i < points.size(); ++i) {
            final double distance = distance(points.get(i), center);
            if (distance > maxDistance) {
                maxDistance     = distance;
                selectedCluster = cluster;
                selectedPoint   = i;
            }
        }

    }

    // did we find at least one non-empty cluster ?
    if (selectedCluster == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    return selectedCluster.getPoints().remove(selectedPoint);

}
Example 20
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest number of points
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestNumberCluster(final Collection<? extends Cluster<T>> clusters)
        throws ConvergenceException {

    int maxNumber = 0;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {

        // get the number of points of the current cluster
        final int number = cluster.getPoints().size();

        // select the cluster with the largest number of points
        if (number > maxNumber) {
            maxNumber = number;
            selected = cluster;
        }

    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}