Examples of de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering

Package de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Examples of de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering

de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering
Pseudo clustering using labels. This "algorithm" puts elements into the same cluster when they agree in their labels. I.e. it just uses a predefined clustering, and is mostly useful for testing and evaluation (e.g. comparing the result of a real algorithm to a reference result / golden standard). If an assignment of an object to multiple clusters is desired, the labels of the object indicating the clusters need to be separated by blanks and the flag {@link #MULTIPLE_ID} needs to be set.TODO: handling of data sets with no labels? @author Erich Schubert @apiviz.uses de.lmu.ifi.dbs.elki.data.ClassLabel


    if(logger.isVerbose()) {
      logger.verbose("Preprocessing clusters...");
    }
    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelClustering()).run(database).getAllClusters();


    // Compute cluster averages and covariance matrix
    HashMap<Cluster<?>, V> averages = new HashMap<Cluster<?>, V>(split.size());
    HashMap<Cluster<?>, Matrix> covmats = new HashMap<Cluster<?>, Matrix>(split.size());
    for(Cluster<?> clus : split) {

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    // run all-in-noise
    TrivialAllNoise allinnoise = new TrivialAllNoise();
    Clustering<Model> ran = allinnoise.run(db);


    // run by-label
    ByLabelClustering bylabel = new ByLabelClustering();
    Clustering<?> rbl = bylabel.run(db);


    assertEquals(1.0, PairCountingFMeasure.compareClusterings(rai, rai), Double.MIN_VALUE);
    assertEquals(1.0, PairCountingFMeasure.compareClusterings(ran, ran), Double.MIN_VALUE);
    assertEquals(1.0, PairCountingFMeasure.compareClusterings(rbl, rbl), Double.MIN_VALUE);

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