Example usage for org.apache.commons.math3.ml.clustering Cluster Cluster

Introduction

In this page you can find the example usage for org.apache.commons.math3.ml.clustering Cluster Cluster.

Prototype

public Cluster() 

Source Link

Document

Build a cluster centered at a specified point.

Usage

From source file:com.yahoo.egads.utilities.DBSCANClusterer.java

/**
 * Performs DBSCAN cluster analysis./*w  ww. ja v a  2s  .  co m*/
 *
 * @param points the points to cluster
 * @return the list of clusters
 * @throws NullArgumentException if the data points are null
 */
public List<Cluster<T>> cluster(final Collection<T> points) throws NullArgumentException {

    // sanity checks
    MathUtils.checkNotNull(points);

    final List<Cluster<T>> clusters = new ArrayList<Cluster<T>>();
    final List<Cluster<T>> anomalousClusters = new ArrayList<Cluster<T>>();
    final Cluster<T> anomalyCluster = new Cluster<T>();
    final Map<Clusterable, PointStatus> visited = new HashMap<Clusterable, PointStatus>();

    for (final T point : points) {
        if (visited.get(point) != null) {
            continue;
        }
        final List<T> neighbors = getNeighbors(point, points);
        if (neighbors.size() >= minPts) {
            // DBSCAN does not care about center points
            final Cluster<T> cluster = new Cluster<T>();
            clusters.add(expandCluster(cluster, point, neighbors, points, visited));
        } else {
            visited.put(point, PointStatus.NOISE);
            anomalyCluster.addPoint(point);
        }
    }
    anomalousClusters.add(anomalyCluster);

    return anomalousClusters;
}

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From source file:utils.DBSCAN.TrajDbscan.java

/**
 * Performs DBSCAN cluster analysis./*w ww  . j  a  v a2 s.  co m*/
 *
 * @param points the points to cluster
 * @return the list of clusters
 * @throws NullArgumentException if the data points are null
 */
@Override
public List<Cluster<T>> cluster(final Collection<T> points) throws NullArgumentException {

    // sanity checks
    MathUtils.checkNotNull(points);

    final List<Cluster<T>> clusters = new ArrayList<Cluster<T>>();
    final Map<Clusterable, PointStatus> visited = new HashMap<Clusterable, PointStatus>();

    for (final T point : points) {
        if (visited.get(point) != null) {
            continue;
        }
        final List<T> neighbors = getNeighbors(point, points);
        if (neighbors.size() >= minPts) {
            // DBSCAN does not care about center points
            final Cluster<T> cluster = new Cluster<T>();
            clusters.add(expandCluster(cluster, point, neighbors, points, visited));
        } else {
            visited.put(point, PointStatus.NOISE);
        }
    }

    return clusters;
}

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