Example usage for weka.clusterers NumberOfClustersRequestable interface-usage

Introduction

In this page you can find the example usage for weka.clusterers NumberOfClustersRequestable interface-usage.

Usage

From source file adaptedClusteringAlgorithms.MySimpleKMeans.java

/**
 * <!-- globalinfo-start --> Cluster data using the k means algorithm
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:

From source file br.ufrn.ia.core.clustering.EMIaProject.java

public class EMIaProject extends RandomizableDensityBasedClusterer
        implements NumberOfClustersRequestable, WeightedInstancesHandler {

    static final long serialVersionUID = 8348181483812829475L;

    private static double m_normConst = Math.log(Math.sqrt(2 * Math.PI));

From source file br.ufrn.ia.core.clustering.SimpleKMeansIaProject.java

public class SimpleKMeansIaProject extends RandomizableClusterer
        implements NumberOfClustersRequestable, WeightedInstancesHandler {

    private static final long serialVersionUID = 8850016179481731406L;

    private ReplaceMissingValues m_ReplaceMissingFilter;

From source file clusterer.SimpleKMeansWithSilhouette.java

/**
 * <!-- globalinfo-start --> Cluster data using the k means algorithm. Can use
 * either the Euclidean distance (default) or the Manhattan distance. If the
 * Manhattan distance is used, then centroids are computed as the component-wise
 * median rather than mean. For more information see:<br/>
 * <br/>

From source file cn.edu.xmu.dm.d3c.clustering.SimpleKMeans.java

/**
 <!-- globalinfo-start -->
 * Cluster data using the k means algorithm. Can use either the Euclidean distance (default) or the Manhattan distance. If the Manhattan distance is used, then centroids are computed as the component-wise median rather than mean. For more information see:<br/>
 * <br/>
 * D. Arthur, S. Vassilvitskii: k-means++: the advantages of carefull seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.
 * <p/>

From source file de.unimannheim.dws.algorithms.CustomSimpleKMedian.java

/**
 * <!-- globalinfo-start --> Cluster data using the k means algorithm
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:

From source file de.unimannheim.dws.algorithms.CustomSimpleKMedoids.java

/**
 * <!-- globalinfo-start --> Cluster data using the k means algorithm
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:

From source file gr.iti.mklab.visual.quantization.SimpleKMeansWithOutput.java

/**
 * <!-- globalinfo-start --> Cluster data using the k means algorithm. Can use either the Euclidean distance
 * (default) or the Manhattan distance. If the Manhattan distance is used, then centroids are computed as the
 * component-wise median rather than mean. For more information see:<br/>
 * <br/>
 * D. Arthur, S. Vassilvitskii: k-means++: the advantages of carefull seeding. In: Proceedings of the

From source file mulan.classifier.meta.ConstrainedKMeans.java

/**
<!-- globalinfo-start -->
 * Cluster data using the k means algorithm
 * <p/>
<!-- globalinfo-end -->
 *

From source file myclusterer.MyAgnes.java

/**
 *
 * @author Visat
 */
public class MyAgnes extends AbstractClusterer implements NumberOfClustersRequestable {

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