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package de.lmu.ifi.dbs.elki.algorithm.clustering.correlation;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2011
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedDBSCAN;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj.FourCSubspaceIndex;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
/**
* 4C identifies local subgroups of data objects sharing a uniform correlation.
* The algorithm is based on a combination of PCA and density-based clustering
* (DBSCAN).
* <p>
* Reference: Christian Böhm, Karin Kailing, Peer Kröger, Arthur Zimek:
* Computing Clusters of Correlation Connected Objects. <br>
* In Proc. ACM SIGMOD Int. Conf. on Management of Data, Paris, France, 2004.
* </p>
*
* @author Arthur Zimek
*
* @apiviz.uses FourCSubspaceIndex
*
* @param <V> type of NumberVector handled by this Algorithm
*/
@Title("4C: Computing Correlation Connected Clusters")
@Description("4C identifies local subgroups of data objects sharing a uniform correlation. " + "The algorithm is based on a combination of PCA and density-based clustering (DBSCAN).")
@Reference(authors = "C. Böhm, K. Kailing, P. Kröger, A. Zimek", title = "Computing Clusters of Correlation Connected Objects", booktitle = "Proc. ACM SIGMOD Int. Conf. on Management of Data, Paris, France, 2004, 455-466", url = "http://dx.doi.org/10.1145/1007568.1007620")
public class FourC<V extends NumberVector<V, ?>> extends AbstractProjectedDBSCAN<Clustering<Model>, V> {
/**
* The logger for this class.
*/
private static final Logging logger = Logging.getLogger(FourC.class);
/**
* Constructor.
*
* @param epsilon Epsilon value
* @param minpts MinPts value
* @param distanceFunction Distance function
* @param lambda Lambda value
*/
public FourC(DoubleDistance epsilon, int minpts, LocallyWeightedDistanceFunction<V> distanceFunction, int lambda) {
super(epsilon, minpts, distanceFunction, lambda);
}
@Override
public String getLongResultName() {
return "4C Clustering";
}
@Override
public String getShortResultName() {
return "4c-clustering";
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(TypeUtil.NUMBER_VECTOR_FIELD);
}
@Override
protected Logging getLogger() {
return logger;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<O extends NumberVector<O, ?>> extends AbstractProjectedDBSCAN.Parameterizer<O, DoubleDistance> {
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
configInnerDistance(config);
configEpsilon(config, innerdist);
configMinPts(config);
configOuterDistance(config, epsilon, minpts, FourCSubspaceIndex.Factory.class, innerdist);
configLambda(config);
}
@Override
protected FourC<O> makeInstance() {
return new FourC<O>(epsilon, minpts, outerdist, lambda);
}
}
}
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