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package de.lmu.ifi.dbs.elki.algorithm.outlier.clustering;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2014
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 java.util.List;
import de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans;
import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd;
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.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.ModelUtil;
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.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.relation.DoubleRelation;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedDoubleRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.database.relation.RelationUtil;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta;
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.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
/**
* Outlier detection by using k-means clustering.
*
* The scores are assigned by the objects distance to the nearest center.
*
* We don't have a clear reference for this approach, but it seems to be a best
* practise in some areas to remove objects that have the largest distance from
* their center. If you need to cite this approach, please cite the ELKI version
* you used (use the <a href="http://elki.dbs.ifi.lmu.de/wiki/Publications">ELKI
* publication list</a> for citation information and BibTeX templates).
*
* @author Erich Schubert
*
* @apiviz.has KMeans
*
* @param <O> Object type
*/
public class KMeansOutlierDetection<O extends NumberVector> extends AbstractAlgorithm<OutlierResult> implements OutlierAlgorithm {
/**
* Class logger.
*/
private static final Logging LOG = Logging.getLogger(KMeansOutlierDetection.class);
/**
* Clustering algorithm to use
*/
KMeans<O, ?> clusterer;
/**
* Constructor.
*
* @param clusterer Clustering algorithm
*/
public KMeansOutlierDetection(KMeans<O, ?> clusterer) {
super();
this.clusterer = clusterer;
}
/**
* Run the outlier detection algorithm.
*
* @param database Database
* @param relation Relation
* @return Outlier detection result
*/
public OutlierResult run(Database database, Relation<O> relation) {
DistanceFunction<? super O> df = clusterer.getDistanceFunction();
DistanceQuery<O> dq = database.getDistanceQuery(relation, df);
// TODO: improve ELKI api to ensure we're using the same DBIDs!
Clustering<?> c = clusterer.run(database, relation);
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_DB);
DoubleMinMax mm = new DoubleMinMax();
@SuppressWarnings("unchecked")
NumberVector.Factory<O> factory = (NumberVector.Factory<O>) RelationUtil.assumeVectorField(relation).getFactory();
List<? extends Cluster<?>> clusters = c.getAllClusters();
for(Cluster<?> cluster : clusters) {
// FIXME: use a primitive distance function on number vectors instead.
O mean = factory.newNumberVector(ModelUtil.getPrototype(cluster.getModel(), relation));
for(DBIDIter iter = cluster.getIDs().iter(); iter.valid(); iter.advance()) {
double dist = dq.distance(mean, iter);
scores.put(iter, dist);
mm.put(dist);
}
}
// Build result representation.
DoubleRelation scoreResult = new MaterializedDoubleRelation("KMeans outlier scores", "kmeans-outlier", scores, relation.getDBIDs());
OutlierScoreMeta scoreMeta = new BasicOutlierScoreMeta(mm.getMin(), mm.getMax(), 0., Double.POSITIVE_INFINITY, 0.);
return new OutlierResult(scoreMeta, scoreResult);
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(clusterer.getDistanceFunction().getInputTypeRestriction());
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterizer.
*
* @author Erich Schubert
*
* @apiviz.exclude
*
* @param <O> Object type
*/
public static class Parameterizer<O extends NumberVector> extends AbstractParameterizer {
/**
* Parameter for choosing the clustering algorithm.
*/
public static final OptionID CLUSTERING_ID = new OptionID("kmeans.algorithm", //
"Clustering algorithm to use for detecting outliers.");
/**
* Clustering algorithm to use
*/
KMeans<O, ?> clusterer;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
ObjectParameter<KMeans<O, ?>> clusterP = new ObjectParameter<>(CLUSTERING_ID, KMeans.class, KMeansLloyd.class);
if(config.grab(clusterP)) {
clusterer = clusterP.instantiateClass(config);
}
}
@Override
protected KMeansOutlierDetection<O> makeInstance() {
return new KMeansOutlierDetection<>(clusterer);
}
}
}
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