Simple K-Means doesnt handle iris.arff

I have this class below, i build it considering the examples given on the wiki and in a thesis, why can't SympleKMeans handle data? The class can pr开发者_Go百科int the Datasource dados, so its nothing wrong with processing file, the error is on the build.

package slcct;

import weka.clusterers.ClusterEvaluation;
import weka.clusterers.SimpleKMeans;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;


public class Cluster {

public String path;
public Instances dados;
public String[] options = new String[2];

public Cluster(String caminho, int nclusters, int seed ){
    this.path = caminho;
    this.options[0] = String.valueOf(nclusters);
    this.options[1] = String.valueOf(seed);

}

public void ledados() throws Exception{

    DataSource source = new DataSource(path);
    dados = source.getDataSet();
    System.out.println(dados)

    if(dados.classIndex()==-1){
        dados.setClassIndex(dados.numAttributes()-1);
    }
}

public void imprimedados(){
    for(int i=0; i<dados.numInstances();i++)
    {
        Instance actual = dados.instance(i);
        System.out.println((i+1) + " : "+ actual);
    }
}

public void clustering() throws Exception{

    SimpleKMeans cluster = new SimpleKMeans();
    cluster.setOptions(options);
    cluster.setDisplayStdDevs(true);
    cluster.getMaxIterations();
    cluster.buildClusterer(dados);

    Instances ClusterCenter = cluster.getClusterCentroids();
    Instances SDev = cluster.getClusterStandardDevs();
    int[] ClusterSize = cluster.getClusterSizes(); 

    ClusterEvaluation eval = new ClusterEvaluation();
    eval.setClusterer(cluster);
    eval.evaluateClusterer(dados);

    for(int i=0;i<ClusterCenter.numInstances();i++){
        System.out.println("Cluster#"+( i +1)+ ": "+ClusterSize[i]+" dados .");
        System.out.println("Centróide:"+ ClusterCenter.instance(i));
        System.out.println("STDDEV:" + SDev.instance(i));
        System.out.println("Cluster Evaluation:"+eval.clusterResultsToString());

    }

}
}

The error:

weka.core.WekaException: weka.clusterers.SimpleKMeans: Cannot handle any class attribute!

at weka.core.Capabilities.test(Capabilities.java:1097)    
at weka.core.Capabilities.test(Capabilities.java:1018)   
at weka.core.Capabilities.testWithFail(Capabilities.java:1297) 
at weka.clusterers.SimpleKMeans.buildClusterer(SimpleKMeans.java:228)    
at slcct.Cluster.clustering(Cluster.java:53)//Here.    
at slcct.Clustering.jButton1ActionPerformed(Clustering.java:104)

I believe you need not set the class index, as you are doing clustering and not classification. Try following this guide for programmatic Java clustering.

In your "ledados()" function just remove the code block given below. It will work. Because you have no defined class in your data.

if(dados.classIndex()==-1){
    dados.setClassIndex(dados.numAttributes()-1);
}

Your new function:

public void ledados() throws Exception{

DataSource source = new DataSource(path);
dados = source.getDataSet();
System.out.println(dados) }

You would not need a class attribute in the data while doing k clustering