How to use

DensityBasedClustererSplitEvaluator

in

weka.experiment

/**
 * Sets the clusterer.
 *
 * @param newClusterer the new clusterer to use.
 */
public void setClusterer(DensityBasedClusterer newClusterer) {
 m_clusterer = newClusterer;
 updateOptions();
}

setClusterer((DensityBasedClusterer) AbstractClusterer.forName(cName, null));
if (getClusterer() instanceof OptionHandler) {
 ((OptionHandler) getClusterer()).setOptions(Utils
  .partitionOptions(options));
 updateOptions();

/**
 * Gets the current settings of the Classifier.
 *
 * @return an array of strings suitable for passing to setOptions
 */
@Override
public String[] getOptions() {
 Vector<String> result;
 String[] clustererOptions;
 result = new Vector<String>();
 clustererOptions = new String[0];
 if ((m_clusterer != null) && (m_clusterer instanceof OptionHandler)) {
  clustererOptions = ((OptionHandler) m_clusterer).getOptions();
 }
 if (getClusterer() != null) {
  result.add("-W");
  result.add(getClusterer().getClass().getName());
 }
 if (getNoSizeDetermination()) {
  result.add("-no-size");
 }
 result.add("--");
 result.addAll(Arrays.asList(clustererOptions));
 return result.toArray(new String[result.size()]);
}

/**
 * Set the Clusterer to use, given it's class name. A new clusterer will be
 * instantiated.
 *
 * @param newClustererName the clusterer class name.
 * @exception Exception if the class name is invalid.
 */
public void setClustererName(String newClustererName) throws Exception {
 try {
  setClusterer((DensityBasedClusterer) Class.forName(newClustererName)
   .newInstance());
 } catch (Exception ex) {
  throw new Exception("Can't find Clusterer with class name: "
   + newClustererName);
 }
}

 return "<null> clusterer";
result.append(toString());
result.append("Clustering model: \n" + m_clusterer.toString() + '\n');

setClusterer((DensityBasedClusterer) AbstractClusterer.forName(cName, null));
if (getClusterer() instanceof OptionHandler) {
 ((OptionHandler) getClusterer()).setOptions(Utils
  .partitionOptions(options));
 updateOptions();

/**
 * Gets the current settings of the Classifier.
 *
 * @return an array of strings suitable for passing to setOptions
 */
@Override
public String[] getOptions() {
 Vector<String> result;
 String[] clustererOptions;
 result = new Vector<String>();
 clustererOptions = new String[0];
 if ((m_clusterer != null) && (m_clusterer instanceof OptionHandler)) {
  clustererOptions = ((OptionHandler) m_clusterer).getOptions();
 }
 if (getClusterer() != null) {
  result.add("-W");
  result.add(getClusterer().getClass().getName());
 }
 if (getNoSizeDetermination()) {
  result.add("-no-size");
 }
 result.add("--");
 result.addAll(Arrays.asList(clustererOptions));
 return result.toArray(new String[result.size()]);
}

/**
 * Set the Clusterer to use, given it's class name. A new clusterer will be
 * instantiated.
 *
 * @param newClustererName the clusterer class name.
 * @exception Exception if the class name is invalid.
 */
public void setClustererName(String newClustererName) throws Exception {
 try {
  setClusterer((DensityBasedClusterer) Class.forName(newClustererName)
   .newInstance());
 } catch (Exception ex) {
  throw new Exception("Can't find Clusterer with class name: "
   + newClustererName);
 }
}

 return "<null> clusterer";
result.append(toString());
result.append("Clustering model: \n" + m_clusterer.toString() + '\n');

/**
 * Sets the clusterer.
 *
 * @param newClusterer the new clusterer to use.
 */
public void setClusterer(DensityBasedClusterer newClusterer) {
 m_clusterer = newClusterer;
 updateOptions();
}

public DensityBasedClustererSplitEvaluator() {
 updateOptions();
}

public DensityBasedClustererSplitEvaluator() {
 updateOptions();
}