Example usage for weka.clusterers HierarchicalClusterer numberOfClusters

Introduction

In this page you can find the example usage for weka.clusterers HierarchicalClusterer numberOfClusters.

Prototype

@Override
    public int numberOfClusters() throws Exception 

Source Link

Usage

From source file:gr.iit.demokritos.cru.cps.ai.KeyphraseClustering.java

License:Open Source License

public ArrayList<String> getClusters() throws Exception {
    System.out.println("Clustering......");
    // int[] clusters_size = new int[clusters];
    HierarchicalClusterer cl = new HierarchicalClusterer();
    // EM em=new EM();
    // XMeans xm = new XMeans();       no nominal attributes
    // DBSCAN db= new DBSCAN();        not our distance function
    // CascadeSimpleKMeans c = new CascadeSimpleKMeans(); not our distance function  

    cl.setNumClusters(this.clusters);
    if (language.equals("en")) {
        // cl.setDistanceFunction(wd);
        //xm.setDistanceF(wd);
        cl.setDistanceFunction(wd);/*from w  w  w .  j a  v a  2s  .  co  m*/
    } else if (language.equals("de")) {
        cl.setDistanceFunction(wdde);
        //c.setDistanceFunction(wdde);
        //xm.setDistanceF(wdde);
    } else if (language.equals("el")) {
        cl.setDistanceFunction(wdel);
        //c.setDistanceFunction(wdel);
        // xm.setDistanceF(wdel);
    }
    cl.buildClusterer(data);
    //xm.buildClusterer(data);
    //c.setMaxIterations(5);
    ArrayList<String> clustersList = new ArrayList<String>();
    for (int i = 0; i < cl.numberOfClusters(); i++) {
        clustersList.add("");
    }
    //cl.buildClusterer(data);
    //em.buildClusterer(data);
    // xm.buildClusterer(data);

    for (int j = 0; j < data.numInstances(); j++) {
        //double[] prob = c.distributionForInstance(data.instance(j));
        //double[] prob = cl.distributionForInstance(data.instance(j)); 
        String clusterLine = data.instance(j).stringValue(0);

        int clust = cl.clusterInstance(data.instance(j));
        clustersList.set(clust, clustersList.get(clust).concat(clusterLine + ";"));
        //take the probabilities prob[i] that it is in the coresponding cluster i
        /*for (int i = 0; i < prob.length; i++) {
         //keep the cluster that has prob>0.9, as this is the cluster that the word is in
         if (prob[i] > 0.9) {
         //keep for every cluster its terms
         clustersList.set(i, clustersList.get(i).concat(clusterLine + ";"));
         //keep the size of cluster i
         // clusters_size[i] = clusters_size[i] + 1;
         }
         }*/
    }
    return clustersList;
}

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From source file:wekimini.InputGenerator.java

public void selectHiClusters() throws Exception {
    buildDataset();/*from  w w w  . j  a  v  a 2s. c  om*/

    HierarchicalClusterer clusterer = new HierarchicalClusterer();
    clusterer.setLinkType(new SelectedTag(1, HierarchicalClusterer.TAGS_LINK_TYPE));

    clusterer.buildClusterer(dataset);

    logger.log(Level.SEVERE, "EM options: ", clusterer.getOptions());
    logger.log(Level.SEVERE, "EM numCluster tip text: ", clusterer.numClustersTipText());
    logger.log(Level.SEVERE, "EM debug tip text: ", clusterer.debugTipText());
    logger.log(Level.SEVERE, "EM info: ", clusterer.globalInfo());

    int num = clusterer.numberOfClusters();
    String txt = Integer.toString(num);
    logger.log(Level.SEVERE, "EM numberOfClusters: ", txt);

    String text = Integer.toString(numClusters);
    logger.log(Level.SEVERE, "EM numClusters: ", text);
}

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