nl.uva.sne.classifiers.Kmeans.java Source code

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Here is the source code for nl.uva.sne.classifiers.Kmeans.java

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package nl.uva.sne.classifiers;

import java.io.IOException;
import java.util.Map;
import java.util.Properties;
import java.util.Random;
import java.util.logging.Level;
import java.util.logging.Logger;
import nl.uva.sne.commons.ClusterUtils;
import org.json.simple.parser.ParseException;
import weka.clusterers.SimpleKMeans;
import weka.core.DistanceFunction;
import weka.core.EuclideanDistance;
import weka.core.Instances;
import weka.core.ManhattanDistance;

/**
 *
 * @author S. Koulouzis
 */
public class Kmeans implements Clusterer {

    private int numOfClusters;
    private String distanceFunction;

    @Override
    public void configure(Properties properties) {
        numOfClusters = Integer.valueOf(properties.getProperty("kmeans.num.of.clusters", "6"));
        distanceFunction = properties.getProperty("distance.function", "Euclidean");
        Logger.getLogger(Kmeans.class.getName()).log(Level.INFO, "kmeans.num.of.clusters: {0}", numOfClusters);
    }

    @Override
    public Map<String, String> cluster(String inDir) throws IOException, ParseException {
        try {

            Instances data = ClusterUtils.terms2Instances(inDir, false);

            DistanceFunction df;
            //            SimpleKMeans currently only supports the Euclidean and Manhattan distances.
            switch (distanceFunction) {
            case "Euclidean":
                df = new EuclideanDistance(data);
                break;
            case "Manhattan":
                df = new ManhattanDistance(data);
                break;
            default:
                df = new EuclideanDistance(data);
                break;
            }

            SimpleKMeans clusterer = new SimpleKMeans();

            Random rand = new Random(System.currentTimeMillis());
            int seed = rand.nextInt((Integer.MAX_VALUE - 1000000) + 1) + 1000000;
            clusterer.setSeed(seed);
            clusterer.setMaxIterations(1000000000);
            Logger.getLogger(Kmeans.class.getName()).log(Level.INFO, "Start clusteing");
            clusterer.setPreserveInstancesOrder(true);

            clusterer.setNumClusters(numOfClusters);
            clusterer.setDistanceFunction(df);

            return ClusterUtils.bulidClusters(clusterer, data, inDir);

        } catch (Exception ex) {
            Logger.getLogger(Kmeans.class.getName()).log(Level.SEVERE, null, ex);
        }
        return null;
    }

}