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package de.lmu.ifi.dbs.elki.algorithm.outlier.distance;
/*
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 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.DoubleDataStore;
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.query.range.RangeQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.utilities.Alias;
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;
/**
* Compute percentage of neighbors in the given neighborhood with size d.
*
* Generalization of the DB Outlier Detection by using the fraction as outlier
* score thus eliminating this parameter and turning the method into a ranking
* method instead of a labelling one.
*
* Reference:
* <p>
* E.M. Knorr, R. T. Ng:<br />
* Algorithms for Mining Distance-Based Outliers in Large Datasets,<br />
* In: Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998.
* </p>
*
* @author Lisa Reichert
*
* @apiviz.has RangeQuery
*
* @param <O> Database object type
*/
@Title("Distance based outlier score")
@Description("Generalization of the original DB-Outlier approach to a ranking method, "//
+ "by turning the fraction parameter into the output value.")
@Reference(prefix = "Generalization of a method proposed in", //
authors = "E.M. Knorr, R. T. Ng", //
title = "Algorithms for Mining Distance-Based Outliers in Large Datasets", //
booktitle = "Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998")
@Alias({ "de.lmu.ifi.dbs.elki.algorithm.outlier.DBOutlierScore" })
public class DBOutlierScore<O> extends AbstractDBOutlier<O> {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(DBOutlierScore.class);
/**
* Constructor with parameters.
*
* @param distanceFunction Distance function
* @param d distance radius parameter
*/
public DBOutlierScore(DistanceFunction<O> distanceFunction, double d) {
super(distanceFunction, d);
}
@Override
protected DoubleDataStore computeOutlierScores(Database database, Relation<O> relation, double d) {
DistanceQuery<O> distFunc = database.getDistanceQuery(relation, getDistanceFunction());
RangeQuery<O> rangeQuery = database.getRangeQuery(distFunc);
final double size = distFunc.getRelation().size();
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(distFunc.getRelation().getDBIDs(), DataStoreFactory.HINT_STATIC);
// TODO: use bulk when implemented.
for(DBIDIter iditer = distFunc.getRelation().iterDBIDs(); iditer.valid(); iditer.advance()) {
// compute percentage of neighbors in the given neighborhood with size d
double n = (rangeQuery.getRangeForDBID(iditer, d).size()) / size;
scores.putDouble(iditer, 1.0 - n);
}
return scores;
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<O> extends AbstractDBOutlier.Parameterizer<O> {
@Override
protected DBOutlierScore<O> makeInstance() {
return new DBOutlierScore<>(distanceFunction, d);
}
}
}
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