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package de.lmu.ifi.dbs.elki.algorithm.outlier.subspace;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
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.AbstractAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.SubspaceClusteringAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm;
import de.lmu.ifi.dbs.elki.data.Cluster;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.model.SubspaceModel;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.result.outlier.InvertedOutlierScoreMeta;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
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.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
/**
* OutRank: ranking outliers in high dimensional data.
*
* Algorithm to score outliers based on a subspace clustering result. This class
* implements score 1 of the OutRank publication, which is a score based on
* cluster sizes and cluster dimensionality.
*
* Reference:
* <p>
* Emmanuel Müller, Ira Assent, Uwe Steinhausen, Thomas Seidl<br />
* OutRank: ranking outliers in high dimensional data<br />
* In Proceedings 24th International Conference on Data Engineering (ICDE)
* Workshop on Ranking in Databases (DBRank), Cancun, Mexico
* </p>
*
* @author Erich Schubert
*/
@Title("OutRank: ranking outliers in high dimensional data")
@Description("Ranking outliers in high dimensional data - score 1")
@Reference(authors = "Emmanuel Müller, Ira Assent, Uwe Steinhausen, Thomas Seidl", title = "OutRank: ranking outliers in high dimensional data", booktitle = "Proc. 24th Int. Conf. on Data Engineering (ICDE) Workshop on Ranking in Databases (DBRank), Cancun, Mexico", url = "http://dx.doi.org/10.1109/ICDEW.2008.4498387")
public class OutRankS1 extends AbstractAlgorithm<OutlierResult> implements OutlierAlgorithm {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(OutRankS1.class);
/**
* Clustering algorithm to run.
*/
protected SubspaceClusteringAlgorithm<? extends SubspaceModel<?>> clusteralg;
/**
* Weighting parameter of size vs. dimensionality score.
*/
double alpha;
/**
* Constructor.
*
* @param clusteralg Clustering algorithm to use (must implement
* {@link SubspaceClusteringAlgorithm}!)
* @param alpha Alpha parameter to balance size and dimensionality.
*/
public OutRankS1(SubspaceClusteringAlgorithm<? extends SubspaceModel<?>> clusteralg, double alpha) {
super();
this.clusteralg = clusteralg;
this.alpha = alpha;
}
@Override
public OutlierResult run(Database database) {
DBIDs ids = database.getRelation(TypeUtil.DBID).getDBIDs();
// Run the primary algorithm
Clustering<? extends SubspaceModel<?>> clustering = clusteralg.run(database);
WritableDoubleDataStore score = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT);
for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
score.putDouble(iter, 0);
}
int maxdim = 0, maxsize = 0;
// Find maximum dimensionality and cluster size
for (Cluster<? extends SubspaceModel<?>> cluster : clustering.getAllClusters()) {
maxsize = Math.max(maxsize, cluster.size());
maxdim = Math.max(maxdim, cluster.getModel().getDimensions().cardinality());
}
// Iterate over all clusters:
DoubleMinMax minmax = new DoubleMinMax();
for (Cluster<? extends SubspaceModel<?>> cluster : clustering.getAllClusters()) {
double relsize = cluster.size() / (double) maxsize;
double reldim = cluster.getModel().getDimensions().cardinality() / (double) maxdim;
// Process objects in the cluster
for (DBIDIter iter = cluster.getIDs().iter(); iter.valid(); iter.advance()) {
double newscore = score.doubleValue(iter) + alpha * relsize + (1 - alpha) * reldim;
score.putDouble(iter, newscore);
minmax.put(newscore);
}
}
Relation<Double> scoreResult = new MaterializedRelation<Double>("OutRank-S1", "OUTRANK_S1", TypeUtil.DOUBLE, score, ids);
OutlierScoreMeta meta = new InvertedOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0, Double.POSITIVE_INFINITY);
OutlierResult res = new OutlierResult(meta, scoreResult);
res.addChildResult(clustering);
return res;
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return clusteralg.getInputTypeRestriction();
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
/**
* Clustering algorithm to use.
*/
public static final OptionID ALGORITHM_ID = new OptionID("outrank.algorithm", "Subspace clustering algorithm to use.");
/**
* Alpha parameter for S1
*/
public static final OptionID ALPHA_ID = new OptionID("outrank.s1.alpha", "Alpha parameter for S1 score.");
/**
* Clustering algorithm to run.
*/
protected SubspaceClusteringAlgorithm<? extends SubspaceModel<?>> algorithm = null;
/**
* Alpha parameter to balance parameters
*/
protected double alpha = 0.25;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
ObjectParameter<SubspaceClusteringAlgorithm<? extends SubspaceModel<?>>> algP = new ObjectParameter<SubspaceClusteringAlgorithm<? extends SubspaceModel<?>>>(ALGORITHM_ID, SubspaceClusteringAlgorithm.class);
if (config.grab(algP)) {
algorithm = algP.instantiateClass(config);
}
DoubleParameter alphaP = new DoubleParameter(ALPHA_ID, 0.25);
alphaP.addConstraint(new GreaterConstraint(0));
if (config.grab(alphaP)) {
alpha = alphaP.doubleValue();
}
}
@Override
protected OutRankS1 makeInstance() {
return new OutRankS1(algorithm, alpha);
}
}
}
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