How to use

buildClusterer

method

in

weka.clusterers.Clusterer

public void train(ListDataSet dataSet) throws Exception {
  instances = new DataSetToInstancesWrapper(dataSet, discrete, false);
  wekaClusterer.buildClusterer(instances);
}

/**
 * Builds the clusters
 */
private void buildClusterer() throws Exception {
 if (m_trainingSet.classIndex() < 0) {
  m_Clusterer.buildClusterer(m_trainingSet);
 } else { // class based evaluation if class attribute is set
  Remove removeClass = new Remove();
  removeClass.setAttributeIndices("" + (m_trainingSet.classIndex() + 1));
  removeClass.setInvertSelection(false);
  removeClass.setInputFormat(m_trainingSet);
  Instances clusterTrain = Filter.useFilter(m_trainingSet, removeClass);
  m_Clusterer.buildClusterer(clusterTrain);
 }
}

/**
 * Builds the clusters
 */
private void buildClusterer() throws Exception {
 if (m_trainingSet.classIndex() < 0) {
  m_Clusterer.buildClusterer(m_trainingSet);
 } else { // class based evaluation if class attribute is set
  Remove removeClass = new Remove();
  removeClass.setAttributeIndices("" + (m_trainingSet.classIndex() + 1));
  removeClass.setInvertSelection(false);
  removeClass.setInputFormat(m_trainingSet);
  Instances clusterTrain = Filter.useFilter(m_trainingSet, removeClass);
  m_Clusterer.buildClusterer(clusterTrain);
 }
}

public Dataset[] cluster(Dataset data) {
  try {
    /*
     * Weka cannot handle class values in clustering, so we have to
     * store and remove them first
     */
    HashMap<Integer, Object> classValueMapping = new HashMap<Integer, Object>();
    for (Instance i : data) {
      classValueMapping.put(i.getID(), i.classValue());
      i.setClassValue(null);
    }
    data.classes().clear();
    /* Convert to Weka and train clustering */
    Instances insts = new ToWekaUtils(data).getDataset();
    Vector<Dataset> output = new Vector<Dataset>();
    wekaCluster.buildClusterer(insts);
    /* Apply clustering to the data set and restore class values */
    for (int i = 0; i < insts.numInstances(); i++) {
      int clusterIndex = wekaCluster.clusterInstance(insts.instance(i));
      while (output.size() <= clusterIndex)
        output.add(new DefaultDataset());
      data.instance(i).setClassValue(classValueMapping.get(data.instance(i).getID()));
      output.get(clusterIndex).add(data.instance(i));
    }
    return output.toArray(new Dataset[output.size()]);
  } catch (Exception e) {
    throw new WekaException(e);
  }
}

clusterer.buildClusterer(train);
println("yes");
result[0] = true;

clusterer.buildClusterer(train);
println("yes");
result[0] = true;

/**
 * Build the clusterer on the filtered data.
 * 
 * @param data the training data
 * @throws Exception if the clusterer could not be built successfully
 */
@Override
public void buildClusterer(Instances data) throws Exception {
 if (m_Clusterer == null) {
  throw new Exception("No base clusterer has been set!");
 }
 // remove instances with missing class
 if (data.classIndex() > -1) {
  data = new Instances(data);
  data.deleteWithMissingClass();
 }
 m_Filter.setInputFormat(data); // filter capabilities are checked here
 data = Filter.useFilter(data, m_Filter);
 // can clusterer handle the data?
 getClusterer().getCapabilities().testWithFail(data);
 m_FilteredInstances = data.stringFreeStructure();
 m_Clusterer.buildClusterer(data);
}

/**
 * Build the clusterer on the filtered data.
 * 
 * @param data the training data
 * @throws Exception if the clusterer could not be built successfully
 */
@Override
public void buildClusterer(Instances data) throws Exception {
 if (m_Clusterer == null) {
  throw new Exception("No base clusterer has been set!");
 }
 // remove instances with missing class
 if (data.classIndex() > -1) {
  data = new Instances(data);
  data.deleteWithMissingClass();
 }
 m_Filter.setInputFormat(data); // filter capabilities are checked here
 data = Filter.useFilter(data, m_Filter);
 // can clusterer handle the data?
 getClusterer().getCapabilities().testWithFail(data);
 m_FilteredInstances = data.stringFreeStructure();
 m_Clusterer.buildClusterer(data);
}

m_ActualClusterer.buildClusterer(toFilterIgnoringAttributes);

m_ActualClusterer.buildClusterer(toFilterIgnoringAttributes);

clusterer.buildClusterer(removedInstances);
numClusters = clusterer.numberOfClusters();

clusterer.buildClusterer(data);

clusterer.buildClusterer(data);

clusterer.buildClusterer(trainingData);

clusterer.buildClusterer(trainingData);

clusterer.buildClusterer(trainCopy);
compareDatasets(train, trainCopy);

clusterer.buildClusterer(trainCopy);
compareDatasets(train, trainCopy);

evaluationB = new ClusterEvaluation();
evaluationI = new ClusterEvaluation();
clusterers[0].buildClusterer(train);
evaluationB.setClusterer(clusterers[0]);
evaluationB.evaluateClusterer(train);
 train.instance(inst).setWeight(weight);
clusterers[1].buildClusterer(train);
built = true;
evaluationI.setClusterer(clusterers[1]);

evaluationB = new ClusterEvaluation();
evaluationI = new ClusterEvaluation();
clusterers[0].buildClusterer(train);
evaluationB.setClusterer(clusterers[0]);
evaluationB.evaluateClusterer(train);
clusterers[1].buildClusterer(new Instances(train, 0));
for (int i = 0; i < train.numInstances(); i++) {
 ((UpdateableClusterer) clusterers[1])

 throw new Exception("No clusterer has been set");
m_wrappedClusterer.buildClusterer(data);
m_model = new DiscreteEstimator[m_wrappedClusterer.numberOfClusters()][data
 .numAttributes()];