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package de.lmu.ifi.dbs.elki.algorithm.outlier.spatial;
/*
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.outlier.spatial.neighborhood.NeighborSetPredicate;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.data.type.VectorFieldTypeInformation;
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.WritableDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
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.math.MeanVariance;
import de.lmu.ifi.dbs.elki.math.statistics.QuickSelect;
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.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
/**
* Median Algorithm of C.-T. Lu
*
* <p>
* Reference: <br>
* C.-T. Lu and D. Chen and Y. Kou<br>
* Algorithms for Spatial Outlier Detection <br>
* in Proc. 3rd IEEE International Conference on Data Mining <br>
* </p>
*
* Median Algorithm uses Median to represent the average non-spatial attribute
* value of neighbors. <br>
* The Difference e = non-spatial-Attribute-Value - Median (Neighborhood) is
* computed.<br>
* The Spatial Objects with the highest standardized e value are Spatial
* Outliers. </p>
*
* @author Ahmed Hettab
*
* @param <N> Neighborhood type
*/
@Title("Median Algorithm for Spatial Outlier Detection")
@Reference(authors = "C.-T. Lu and D. Chen and Y. Kou", title = "Algorithms for Spatial Outlier Detection", booktitle = "Proc. 3rd IEEE International Conference on Data Mining", url="http://dx.doi.org/10.1109/ICDM.2003.1250986")
public class CTLuMedianAlgorithm<N> extends AbstractNeighborhoodOutlier<N> {
/**
* The logger for this class.
*/
private static final Logging logger = Logging.getLogger(CTLuMedianAlgorithm.class);
/**
* Constructor
*
* @param npredf Neighborhood predicate
*/
public CTLuMedianAlgorithm(NeighborSetPredicate.Factory<N> npredf) {
super(npredf);
}
/**
* Main method
*
* @param nrel Neighborhood relation
* @param relation Data relation (1d!)
* @return Outlier detection result
*/
public OutlierResult run(Relation<N> nrel, Relation<? extends NumberVector<?, ?>> relation) {
final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
WritableDataStore<Double> scores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);
MeanVariance mv = new MeanVariance();
for(DBID id : relation.iterDBIDs()) {
DBIDs neighbors = npred.getNeighborDBIDs(id);
final double median;
{
double[] fi = new double[neighbors.size()];
// calculate and store Median of neighborhood
int c = 0;
for(DBID n : neighbors) {
if(id.equals(n)) {
continue;
}
fi[c] = relation.get(n).doubleValue(1);
c++;
}
if(c > 0) {
// Note: only use up to c-1, since we may have used a too big array
median = QuickSelect.median(fi, 0, c - 1);
}
else {
median = relation.get(id).doubleValue(1);
}
}
double h = relation.get(id).doubleValue(1) - median;
scores.put(id, h);
mv.put(h);
}
// Normalize scores
final double mean = mv.getMean();
final double stddev = mv.getNaiveStddev();
DoubleMinMax minmax = new DoubleMinMax();
for(DBID id : relation.iterDBIDs()) {
double score = Math.abs((scores.get(id) - mean) / stddev);
minmax.put(score);
scores.put(id, score);
}
Relation<Double> scoreResult = new MaterializedRelation<Double>("MO", "Median-outlier", TypeUtil.DOUBLE, scores, relation.getDBIDs());
OutlierScoreMeta scoreMeta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0);
OutlierResult or = new OutlierResult(scoreMeta, scoreResult);
or.addChildResult(npred);
return or;
}
@Override
protected Logging getLogger() {
return logger;
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(getNeighborSetPredicateFactory().getInputTypeRestriction(), VectorFieldTypeInformation.get(NumberVector.class, 1));
}
/**
* Parameterization class
*
* @author Ahmed Hettab
*
* @apiviz.exclude
*
* @param <N> Neighborhood object type
*/
public static class Parameterizer<N> extends AbstractNeighborhoodOutlier.Parameterizer<N> {
@Override
protected CTLuMedianAlgorithm<N> makeInstance() {
return new CTLuMedianAlgorithm<N>(npredf);
}
}
}
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