Examples of weka.clusterers.EM

weka.clusterers.EM
Simple EM (expectation maximisation) class.

EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters. EM can decide how many clusters to create by cross validation, or you may specify apriori how many clusters to generate.

The cross validation performed to determine the number of clusters is done in the following steps:
1. the number of clusters is set to 1
2. the training set is split randomly into 10 folds.
3. EM is performed 10 times using the 10 folds the usual CV way.
4. the loglikelihood is averaged over all 10 results.
5. if loglikelihood has increased the number of clusters is increased by 1 and the program continues at step 2.

The number of folds is fixed to 10, as long as the number of instances in the training set is not smaller 10. If this is the case the number of folds is set equal to the number of instances.
Valid options are:
```
 -N <num> number of clusters. If omitted or -1 specified, then  cross validation is used to select the number of clusters.
```
```
 -I <num> max iterations. (default 100)
```
```
 -V verbose.
```
```
 -M <num> minimum allowable standard deviation for normal density computation (default 1e-6)
```
```
 -O Display model in old format (good when there are many clusters) 
```
```
 -S <num> Random number seed. (default 100)
```
@author Mark Hall (mhall@cs.waikato.ac.nz) @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 6299 $


  /**
   * returns a configured cluster algorithm
   */
  protected Clusterer getClusterer() {
    EM c = new EM();
    try {
      c.setOptions(new String[0]);
    }
    catch (Exception e) {
      e.printStackTrace();
    }
    return c;

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        Filter filter = aset.createFilter(trainingData);
        
        // Using our training set to build the clusterer
        int seed = this.rand.nextInt(); 
//        SimpleKMeans inner_clusterer = new SimpleKMeans();
        EM inner_clusterer = new EM();
        String options[] = {
            "-N", Integer.toString(1000), // num_partitions),
            "-S", Integer.toString(seed),
            "-I", Integer.toString(100),
            
        };
        inner_clusterer.setOptions(options);
        
        FilteredClusterer filtered_clusterer = new FilteredClusterer();
        filtered_clusterer.setFilter(filter);
        filtered_clusterer.setClusterer(inner_clusterer);

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  public ClusteringResult performClustering(Instances dataset,
      ParameterSet parameters) {


    List<Integer> clusters = new ArrayList<Integer>();
    String[] options = new String[2];
    EM clusterer = new EM();


    int numberOfIterations = parameters.getParameter(
        EMClustererParameters.numberOfIterations).getValue();
    options[0] = "-I";
    options[1] = String.valueOf(numberOfIterations);


    try {
      clusterer.setOptions(options);
      clusterer.buildClusterer(dataset);
      Enumeration e = dataset.enumerateInstances();
      while (e.hasMoreElements()) {
        clusters.add(clusterer.clusterInstance((Instance) e
            .nextElement()));
      }
      ClusteringResult result = new ClusteringResult(clusters, null,
          clusterer.numberOfClusters(), parameters.getParameter(
              EMClustererParameters.visualization).getValue());
      return result;


    } catch (Exception ex) {
      logger.log(Level.SEVERE, null, ex);

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