Examples with ClusterContingencyTable de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable used on opensource projects

Example 6 with ClusterContingencyTable

use of de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable in project elki by elki-project.

the class ClusteringAdjustedRandIndexSimilarityFunction method distance.

@Override
public double distance(Clustering<?> o1, Clustering<?> o2) {
    ClusterContingencyTable ct = new ClusterContingencyTable(false, true);
    ct.process(o1, o2);
    return 1. - ct.getPaircount().adjustedRandIndex();
}

Example 7 with ClusterContingencyTable

use of de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable in project elki by elki-project.

the class ClusteringAdjustedRandIndexSimilarityFunction method similarity.

@Override
public double similarity(Clustering<?> o1, Clustering<?> o2) {
    ClusterContingencyTable ct = new ClusterContingencyTable(false, true);
    ct.process(o1, o2);
    return ct.getPaircount().adjustedRandIndex();
}

Example 8 with ClusterContingencyTable

use of de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable in project elki by elki-project.

the class ClusteringRandIndexSimilarityFunction method similarity.

@Override
public double similarity(Clustering<?> o1, Clustering<?> o2) {
    ClusterContingencyTable ct = new ClusterContingencyTable(false, true);
    ct.process(o1, o2);
    return ct.getPaircount().randIndex();
}

Example 9 with ClusterContingencyTable

use of de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable in project elki by elki-project.

the class ClusteringBCubedF1SimilarityFunction method similarity.

@Override
public double similarity(Clustering<?> o1, Clustering<?> o2) {
    ClusterContingencyTable ct = new ClusterContingencyTable(false, true);
    ct.process(o1, o2);
    return ct.getBCubed().f1Measure();
}

Example 10 with ClusterContingencyTable

use of de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable in project elki by elki-project.

the class ClusteringFowlkesMallowsSimilarityFunction method distance.

@Override
public double distance(Clustering<?> o1, Clustering<?> o2) {
    ClusterContingencyTable ct = new ClusterContingencyTable(false, true);
    ct.process(o1, o2);
    return 1. - ct.getPaircount().fowlkesMallows();
}