Examples with MinPtsCorePredicate de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.MinPtsCorePredicate used on opensource projects

Example 1 with MinPtsCorePredicate

use of de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.MinPtsCorePredicate in project elki by elki-project.

the class ERiC method run.

/**
 * Performs the ERiC algorithm on the given database.
 *
 * @param relation Relation to process
 * @return Clustering result
 */
public Clustering<CorrelationModel> run(Database database, Relation<V> relation) {
    final int dimensionality = RelationUtil.dimensionality(relation);
    StepProgress stepprog = LOG.isVerbose() ? new StepProgress(3) : null;
    // Run Generalized DBSCAN
    LOG.beginStep(stepprog, 1, "Preprocessing local correlation dimensionalities and partitioning data");
    // FIXME: how to ensure we are running on the same relation?
    ERiCNeighborPredicate<V>.Instance npred = new ERiCNeighborPredicate<V>(settings).instantiate(database, relation);
    CorePredicate.Instance<DBIDs> cpred = new MinPtsCorePredicate(settings.minpts).instantiate(database);
    Clustering<Model> copacResult = new GeneralizedDBSCAN.Instance<>(npred, cpred, false).run();
    // extract correlation clusters
    LOG.beginStep(stepprog, 2, "Extract correlation clusters");
    List<List<Cluster<CorrelationModel>>> clusterMap = extractCorrelationClusters(copacResult, relation, dimensionality, npred);
    if (LOG.isDebugging()) {
        StringBuilder msg = new StringBuilder("Step 2: Extract correlation clusters...");
        for (int corrDim = 0; corrDim < clusterMap.size(); corrDim++) {
            List<Cluster<CorrelationModel>> correlationClusters = clusterMap.get(corrDim);
            msg.append("\n\ncorrDim ").append(corrDim);
            for (Cluster<CorrelationModel> cluster : correlationClusters) {
                msg.append("\n  cluster ").append(cluster).append(", ids: ").append(cluster.getIDs().size());
            // .append(", level: ").append(cluster.getLevel()).append(", index:
            // ").append(cluster.getLevelIndex());
            // msg.append("\n basis " +
            // cluster.getPCA().getWeakEigenvectors().toString(" ", NF) +
            // " ids " + cluster.getIDs().size());
            }
        }
        LOG.debugFine(msg.toString());
    }
    if (LOG.isVerbose()) {
        int clusters = 0;
        for (List<Cluster<CorrelationModel>> correlationClusters : clusterMap) {
            clusters += correlationClusters.size();
        }
        LOG.verbose(clusters + " clusters extracted.");
    }
    // build hierarchy
    LOG.beginStep(stepprog, 3, "Building hierarchy");
    Clustering<CorrelationModel> clustering = new Clustering<>("ERiC clustering", "eric-clustering");
    buildHierarchy(clustering, clusterMap, npred);
    if (LOG.isDebugging()) {
        StringBuilder msg = new StringBuilder("Step 3: Build hierarchy");
        for (int corrDim = 0; corrDim < clusterMap.size(); corrDim++) {
            List<Cluster<CorrelationModel>> correlationClusters = clusterMap.get(corrDim);
            for (Cluster<CorrelationModel> cluster : correlationClusters) {
                msg.append("\n  cluster ").append(cluster).append(", ids: ").append(cluster.getIDs().size());
                // ").append(cluster.getLevelIndex());
                for (It<Cluster<CorrelationModel>> iter = clustering.getClusterHierarchy().iterParents(cluster); iter.valid(); iter.advance()) {
                    msg.append("\n   parent ").append(iter.get());
                }
                for (It<Cluster<CorrelationModel>> iter = clustering.getClusterHierarchy().iterChildren(cluster); iter.valid(); iter.advance()) {
                    msg.append("\n   child ").append(iter.get());
                }
            }
        }
        LOG.debugFine(msg.toString());
    }
    LOG.setCompleted(stepprog);
    for (Cluster<CorrelationModel> rc : clusterMap.get(clusterMap.size() - 1)) {
        clustering.addToplevelCluster(rc);
    }
    return clustering;
}

Example 2 with MinPtsCorePredicate

use of de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.MinPtsCorePredicate in project elki by elki-project.

the class COPAC method run.

/**
 * Run the COPAC algorithm.
 *
 * @param database Database
 * @param relation Vector field relation
 * @return COPAC clustering
 */
public Clustering<DimensionModel> run(Database database, Relation<V> relation) {
    COPACNeighborPredicate.Instance npred = new COPACNeighborPredicate<V>(settings).instantiate(database, relation);
    CorePredicate.Instance<DBIDs> cpred = new MinPtsCorePredicate(settings.minpts).instantiate(database);
    Clustering<Model> dclusters = new GeneralizedDBSCAN.Instance<>(npred, cpred, false).run();
    // Re-wrap the detected clusters for COPAC:
    Clustering<DimensionModel> result = new Clustering<>("COPAC clustering", "copac-clustering");
    // Generalized DBSCAN clusterings will be flat.
    for (It<Cluster<Model>> iter = dclusters.iterToplevelClusters(); iter.valid(); iter.advance()) {
        Cluster<Model> clus = iter.get();
        if (clus.size() > 0) {
            int dim = npred.dimensionality(clus.getIDs().iter());
            DimensionModel model = new DimensionModel(dim);
            result.addToplevelCluster(new Cluster<>(clus.getIDs(), model));
        }
    }
    return result;
}