How to use

clusterInstance

method

in

weka.clusterers.Clusterer

@Override
protected MultiLabelOutput makePredictionInternal(Instance instance) throws Exception, InvalidDataException {
  Instance newInstance = RemoveAllLabels.transformInstance(instance, labelIndices);
  int cluster = clusterer.clusterInstance(newInstance);
  return multi[cluster].makePrediction(instance);
}

/**
 * Add a cluster label to an instance using a clusterer
 *
 * @param clusterer the clusterer to use
 * @param inst the instance to append a prediction to
 * @param instOrig the original instance
 * @throws WekaException if a problem occurs
 */
protected void predictLabelClusterer(weka.clusterers.Clusterer clusterer,
 Instance inst, Instance instOrig) throws WekaException {
 try {
  int cluster = clusterer.clusterInstance(instOrig);
  inst.setValue(inst.numAttributes() - 1, (double) cluster);
 } catch (Exception ex) {
  throw new WekaException(ex);
 }
}

/**
 * Add a cluster label to an instance using a clusterer
 *
 * @param clusterer the clusterer to use
 * @param inst the instance to append a prediction to
 * @param instOrig the original instance
 * @throws WekaException if a problem occurs
 */
protected void predictLabelClusterer(weka.clusterers.Clusterer clusterer,
 Instance inst, Instance instOrig) throws WekaException {
 try {
  int cluster = clusterer.clusterInstance(instOrig);
  inst.setValue(inst.numAttributes() - 1, (double) cluster);
 } catch (Exception ex) {
  throw new WekaException(ex);
 }
}

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);
  }
}

} else {
 try {
  cnum = clusterer.clusterInstance(inst);

} else {
 try {
  cnum = clusterer.clusterInstance(inst);

 loglk +=
  ((DensityBasedClusterer) m_Clusterer).logDensityForInstance(inst);
 cnum = m_Clusterer.clusterInstance(inst);
 clusterAssignments.add((double) cnum);
} else {
 cnum = m_Clusterer.clusterInstance(inst);
 clusterAssignments.add((double) cnum);

 loglk +=
  ((DensityBasedClusterer) m_Clusterer).logDensityForInstance(inst);
 cnum = m_Clusterer.clusterInstance(inst);
 clusterAssignments.add((double) cnum);
} else {
 cnum = m_Clusterer.clusterInstance(inst);
 clusterAssignments.add((double) cnum);

} else {
 try {
  cnum = clusterer.clusterInstance(inst);

} else {
 try {
  cnum = clusterer.clusterInstance(inst);

int clusterOfInstance = clusterer.clusterInstance(removedInstances.instance(i));
subsetMultiLabelInstances[clusterOfInstance].getDataSet().add(trainInstances.instance(i));

result += "\n";
 try {
cluster = clusterer.clusterInstance(data.instance(i));
result += "" + (i+1) + ": " + cluster;

result += "\n";
 try {
cluster = clusterer.clusterInstance(data.instance(i));
result += "" + (i+1) + ": " + cluster;

double predCluster = clusterer.clusterInstance(testSet.instance(i));
newInstances.instance(i).setValue(newInstances.numAttributes() - 1,
 predCluster);

double predCluster = clusterer.clusterInstance(testSet.instance(i));
newInstances.instance(i).setValue(newInstances.numAttributes() - 1,
 predCluster);

  .clusterInstance(filteredI);
} catch (Exception e) {

  .clusterInstance(filteredI);
} catch (Exception e) {

for (int i = 0; i < data.numInstances(); i++) {
 inst = data.instance(i);
 int cluster = m_wrappedClusterer.clusterInstance(inst);
 m_priors[cluster] += inst.weight();
 for (int j = 0; j < data.numAttributes(); j++) {

for (int i = 0; i < data.numInstances(); i++) {
 inst = data.instance(i);
 int cluster = m_wrappedClusterer.clusterInstance(inst);
 m_priors[cluster] += inst.weight();
 for (int j = 0; j < data.numAttributes(); j++) {