model.clustering.Clustering.java Source code

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Here is the source code for model.clustering.Clustering.java

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package model.clustering;

import java.util.Enumeration;
import weka.clusterers.ClusterEvaluation;
import weka.clusterers.FilteredClusterer;
import weka.clusterers.SimpleKMeans;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.unsupervised.attribute.Remove;

/**
 *
 * @author Baki
 */
public class Clustering {

    private SimpleKMeans kMeansCLusterer;
    private ClusterEvaluation eval;

    public Instances loadData(String fileName) throws Exception {
        DataSource loader = new DataSource(fileName);
        Instances data = loader.getDataSet();

        return data;
    }

    //    public String centroids (Instances data) throws Exception {
    //
    //      
    //
    //      SimpleKMeans kMeansCLusterer = new SimpleKMeans();
    //      kMeansCLusterer.setNumClusters(3);
    //      kMeansCLusterer.setDisplayStdDevs(true);
    //
    //      FilteredClusterer filteredClusterer = new FilteredClusterer();
    //      filteredClusterer.setClusterer(kMeansCLusterer);
    //      filteredClusterer.buildClusterer(data);
    //
    //      return kMeansCLusterer.getClusterCentroids().toString();
    //   }
    public String filledFile(Instances data, int numOfClusters, String remove) throws Exception {

        String mainData = data.toString();
        int index = mainData.indexOf("@data");
        String clusterData = mainData.substring(0, index + 6);

        Remove removeFilter = new Remove();
        removeFilter.setAttributeIndices(remove);

        kMeansCLusterer = new SimpleKMeans();
        kMeansCLusterer.setNumClusters(numOfClusters);

        FilteredClusterer filteredClusterer = new FilteredClusterer();
        filteredClusterer.setClusterer(kMeansCLusterer);
        filteredClusterer.setFilter(removeFilter);
        filteredClusterer.buildClusterer(data);

        Enumeration<Instance> newData = data.enumerateInstances();

        eval = new ClusterEvaluation();
        eval.setClusterer(filteredClusterer);
        eval.evaluateClusterer(data);

        while (newData.hasMoreElements()) {

            Instance i = (Instance) newData.nextElement();
            int kluster = filteredClusterer.clusterInstance(i);
            String instanceString = i.toString() + "," + kluster;
            clusterData = clusterData + instanceString + "\n";

        }
        return clusterData;
    }

    public SimpleKMeans getkMeansCLusterer() {
        return kMeansCLusterer;
    }

    public ClusterEvaluation getEval() {
        return eval;
    }

}