Example usage for org.apache.commons.math3.ml.clustering Clusterer subclass-usage

Introduction

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

Usage

From source file org.rhwlab.BHC.BalancedKMeansClusterer.java

/**
 *
 * @author gevirl
 */
public class BalancedKMeansClusterer extends org.apache.commons.math3.ml.clustering.Clusterer<Clusterable> {
    public BalancedKMeansClusterer(int k) {

From source file org.rhwlab.BHC.FreqSensKMeansClusterer.java

/**
 *
 * @author gevirl
 */
public class FreqSensKMeansClusterer extends org.apache.commons.math3.ml.clustering.Clusterer<Clusterable> {
    public FreqSensKMeansClusterer(int k) {

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

/**
 * DBSCAN (density-based spatial clustering of applications with noise) algorithm.
 * <p>
 * The DBSCAN algorithm forms clusters based on the idea of density connectivity, i.e.
 * a point p is density connected to another point q, if there exists a chain of
 * points p<sub>i</sub>, with i = 1 .. n and p<sub>1</sub> = p and p<sub>n</sub> = q,

From source file Clustering.technique.KMeansPlusPlusClusterer.java

/**
 * Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.
 * @param <T> type of the points to cluster
 * @see <a href="http://en.wikipedia.org/wiki/K-means%2B%2B">K-means++ (wikipedia)</a>
 * @version $Id: KMeansPlusPlusClusterer.java 1461866 2013-03-27 21:54:36Z tn $
 * @since 3.2

From source file KMeansRecommender.MyKMeansPlusPlusClusterer.java

/**
 * Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.
 * @param <T> type of the points to cluster
 * @see <a href="http://en.wikipedia.org/wiki/K-means%2B%2B">K-means++ (wikipedia)</a>
 * @version $Id: MyKMeansPlusPlusClusterer.java 1461866 2013-03-27 21:54:36Z tn $
 * @since 3.2

From source file org.esa.s2tbx.s2msi.idepix.operators.cloudshadow.MyClustering.java

/**
 * Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.
 * This has been adapted by Grit and Michael.
 *
 * @param <T> type of the points to cluster
 * @see <a href="http://en.wikipedia.org/wiki/K-means%2B%2B">K-means++ (wikipedia)</a>

From source file utils.DBSCAN.TrajDbscan.java

/**
 * DBSCAN (density-based spatial clustering of applications with noise) algorithm.
 * <p>
 * The DBSCAN algorithm forms clusters based on the idea of density connectivity, i.e.
 * a point p is density connected to another point q, if there exists a chain of
 * points p<sub>i</sub>, with i = 1 .. n and p<sub>1</sub> = p and p<sub>n</sub> = q,