Example usage for weka.clusterers ClusterEvaluation evaluateClusterer

Introduction

In this page you can find the example usage for weka.clusterers ClusterEvaluation evaluateClusterer.

Prototype

public static String evaluateClusterer(Clusterer clusterer, String[] options) throws Exception 

Source Link

Document

Evaluates a clusterer with the options given in an array of strings.

Usage

From source file:detplagiasi.EMClustering.java

EMClustering() {
    addd = ct.getAddress();//from   ww  w  .j  a  v  a2 s  .c om

    try {
        ClusterEvaluation eval;
        Instances data;
        String[] options;
        DensityBasedClusterer cl;

        File he = getArffFile();
        data = new Instances(new BufferedReader(new FileReader(he)));
        System.out.println("-----EM Clustering-----");
        // normal
        try (BufferedWriter out = new BufferedWriter(new FileWriter(addd + "\\output.txt", true))) {
            out.write("\r\n--> normal\r\n");
            options = new String[2];
            options[0] = "-t";
            options[1] = he.getAbsolutePath();
            out.write("\r\n" + ClusterEvaluation.evaluateClusterer(new EM(), options) + "\r\n");
            out.write("\r\n");

            // manual call
            out.write("\n--> manual\r\n");
            cl = new EM();
            out.write("\r\n");
            cl.buildClusterer(data);
            getDataUji();
            getDataTraining();
            System.out.println("jumlah kluster = " + cl.numberOfClusters());
            noClusterUji = cl.clusterInstance(dataUji.instance(0));
            totalCluster = cl.numberOfClusters();
            System.out.println("kluster = " + cl.clusterInstance(dataUji.instance(0)));
            for (int b = 0; b < dataTraining.numInstances(); b++) {
                System.out.print("file " + td.fileName[b] + " termasuk cluster ke ");
                array1[b] = td.fileName[b];
                array2[b] = cl.clusterInstance(dataTraining.instance(b));

                System.out.println(cl.clusterInstance(dataTraining.instance(b)));
                //simpan nilai instance ke dalam sebuah array int buat dikirim ke detplaggui
            }

            out.write("\r\n");

            eval = new ClusterEvaluation();
            eval.setClusterer(cl);
            eval.evaluateClusterer(new Instances(data));
            out.write("\r\n\n# of clusters: " + eval.getNumClusters());

        } catch (Exception e) {
            System.err.println(e.getMessage());
            System.out.println("error2 em cluster");
        }

    } catch (IOException ex) {
        Logger.getLogger(EMClustering.class.getName()).log(Level.SEVERE, null, ex);
        System.out.println("errorrrr null em");
    }
}

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From source file:detplagiasi.KMeansClustering.java

KMeansClustering() {
    addd = Container.getAddress();
    try {//from  www .  j a v a2s  . c  om
        ClusterEvaluation eval;
        Instances data;
        String[] options;
        SimpleKMeans cl;

        File he = getArffFile();
        data = new Instances(new BufferedReader(new FileReader(he)));
        System.out.println("-----KMeans Clustering-----");
        // normal
        try (BufferedWriter out = new BufferedWriter(new FileWriter(addd + "\\output.txt", true))) {
            out.write("\r\n--> normal\r\n");
            options = new String[2];
            options[0] = "-t";
            options[1] = he.getAbsolutePath();
            out.write("\r\n" + ClusterEvaluation.evaluateClusterer(new SimpleKMeans(), options) + "\r\n");
            out.write("\r\n");

            // manual call
            out.write("\n--> manual\r\n");
            cl = new SimpleKMeans();
            cl.setNumClusters(4);
            out.write("\r\n");
            cl.buildClusterer(data);
            getDataUji();
            System.out.println("jumlah kluster = " + cl.numberOfClusters());
            System.out.println("kluster = " + cl.clusterInstance(dataUji.instance(0)));
            noClusterUji = cl.clusterInstance(dataUji.instance(0));
            totalCluster = cl.numberOfClusters();
            for (int b = 0; b < dataTraining.numInstances(); b++) {
                System.out.print("file " + td.fileName[b] + " termasuk cluster ke ");
                System.out.println(cl.clusterInstance(dataTraining.instance(b)));
                array1[b] = td.fileName[b];
                array2[b] = cl.clusterInstance(dataTraining.instance(b));
                //simpan nilai instance ke dalam sebuah array int buat dikirim ke detplaggui
            }

            out.write("\r\n");

            eval = new ClusterEvaluation();
            eval.setClusterer(cl);
            eval.evaluateClusterer(new Instances(data));
            out.write("\r\n\n# of clusters: " + eval.getNumClusters());

        } catch (Exception e) {
            System.err.println(e.getMessage());
            System.out.println("error2 kmeans cluster");
        }

    } catch (IOException ex) {
        Logger.getLogger(Clustering.class.getName()).log(Level.SEVERE, null, ex);
        System.out.println("errorrrr null kmeans");
    }
}

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