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Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/algorithm/outlier/LDOF.java')
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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/LDOF.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/LDOF.java new file mode 100644 index 00000000..04ffe8cf --- /dev/null +++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/LDOF.java @@ -0,0 +1,202 @@ +package de.lmu.ifi.dbs.elki.algorithm.outlier;
+/* +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 java.util.List;
+
+import de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm;
+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.WritableDataStore;
+import de.lmu.ifi.dbs.elki.database.ids.DBID;
+import de.lmu.ifi.dbs.elki.database.query.DistanceResultPair;
+import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
+import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.distancevalue.NumberDistance;
+import de.lmu.ifi.dbs.elki.logging.Logging;
+import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
+import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
+import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
+import de.lmu.ifi.dbs.elki.result.outlier.QuotientOutlierScoreMeta;
+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.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.IntParameter;
+
+/**
+ * <p>
+ * Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a
+ * Database.
+ * </p>
+ *
+ * <p>
+ * Reference:<br>
+ * K. Zhang, M. Hutter, H. Jin: A New Local Distance-Based Outlier Detection
+ * Approach for Scattered Real-World Data.<br>
+ * In: Proc. 13th Pacific-Asia Conference on Advances in Knowledge Discovery and
+ * Data Mining (PAKDD 2009), Bangkok, Thailand, 2009.
+ * </p>
+ *
+ * @author Arthur Zimek
+ *
+ * @apiviz.has KNNQuery
+ *
+ * @param <O> the type of DatabaseObjects handled by this Algorithm
+ */
+@Title("LDOF: Local Distance-Based Outlier Factor")
+@Description("Local outlier detection appraoch suitable for scattered data by averaging the kNN distance over all k nearest neighbors")
+@Reference(authors = "K. Zhang, M. Hutter, H. Jin", title = "A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data", booktitle = "Proc. 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2009), Bangkok, Thailand, 2009", url = "http://dx.doi.org/10.1007/978-3-642-01307-2_84")
+public class LDOF<O, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm<O, D, OutlierResult> implements OutlierAlgorithm {
+ /**
+ * The logger for this class.
+ */
+ private static final Logging logger = Logging.getLogger(LDOF.class);
+
+ /**
+ * Parameter to specify the number of nearest neighbors of an object to be
+ * considered for computing its LDOF_SCORE, must be an integer greater than 1.
+ */
+ public static final OptionID K_ID = OptionID.getOrCreateOptionID("ldof.k", "The number of nearest neighbors of an object to be considered for computing its LDOF_SCORE.");
+
+ /**
+ * The baseline for LDOF values. The paper gives 0.5 for uniform
+ * distributions, although one might also discuss using 1.0 as baseline.
+ */
+ private static final double LDOF_BASELINE = 0.5;
+
+ /**
+ * Holds the value of {@link #K_ID}.
+ */
+ int k;
+
+ /**
+ * Constructor.
+ *
+ * @param distanceFunction distance function
+ * @param k k Parameter
+ */
+ public LDOF(DistanceFunction<? super O, D> distanceFunction, int k) {
+ super(distanceFunction);
+ this.k = k;
+ }
+
+ public OutlierResult run(Database database, Relation<O> relation) throws IllegalStateException {
+ DistanceQuery<O, D> distFunc = database.getDistanceQuery(relation, getDistanceFunction());
+ KNNQuery<O, D> knnQuery = database.getKNNQuery(distFunc, k);
+
+ // track the maximum value for normalization
+ DoubleMinMax ldofminmax = new DoubleMinMax();
+ // compute the ldof values
+ WritableDataStore<Double> ldofs = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, Double.class);
+
+ // compute LOF_SCORE of each db object
+ if(logger.isVerbose()) {
+ logger.verbose("Computing LDOFs");
+ }
+ FiniteProgress progressLDOFs = logger.isVerbose() ? new FiniteProgress("LDOF_SCORE for objects", relation.size(), logger) : null;
+
+ for(DBID id : distFunc.getRelation().iterDBIDs()) {
+ List<DistanceResultPair<D>> neighbors = knnQuery.getKNNForDBID(id, k);
+ int nsize = neighbors.size() - 1;
+ // skip the point itself
+ double dxp = 0;
+ double Dxp = 0;
+ for(DistanceResultPair<D> neighbor1 : neighbors) {
+ if(neighbor1.getDBID() != id) {
+ dxp += neighbor1.getDistance().doubleValue();
+ for(DistanceResultPair<D> neighbor2 : neighbors) {
+ if(neighbor1.getDBID() != neighbor2.getDBID() && neighbor2.getDBID() != id) {
+ Dxp += distFunc.distance(neighbor1.getDBID(), neighbor2.getDBID()).doubleValue();
+ }
+ }
+ }
+ }
+ dxp /= nsize;
+ Dxp /= (nsize * (nsize - 1));
+ Double ldof = dxp / Dxp;
+ if(ldof.isNaN() || ldof.isInfinite()) {
+ ldof = 1.0;
+ }
+ ldofs.put(id, ldof);
+ // update maximum
+ ldofminmax.put(ldof);
+
+ if(progressLDOFs != null) {
+ progressLDOFs.incrementProcessed(logger);
+ }
+ }
+ if(progressLDOFs != null) {
+ progressLDOFs.ensureCompleted(logger);
+ }
+
+ // Build result representation.
+ Relation<Double> scoreResult = new MaterializedRelation<Double>("LDOF Outlier Score", "ldof-outlier", TypeUtil.DOUBLE, ldofs, relation.getDBIDs());
+ OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(ldofminmax.getMin(), ldofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, LDOF_BASELINE);
+ return new OutlierResult(scoreMeta, scoreResult);
+ }
+
+ @Override
+ public TypeInformation[] getInputTypeRestriction() {
+ return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
+ }
+
+ @Override
+ protected Logging getLogger() {
+ return logger;
+ }
+
+ /**
+ * Parameterization class. + * + * @author Erich Schubert + * + * @apiviz.exclude + */
+ public static class Parameterizer<O, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm.Parameterizer<O, D> {
+ protected int k = 0;
+
+ @Override
+ protected void makeOptions(Parameterization config) {
+ super.makeOptions(config);
+ final IntParameter kP = new IntParameter(K_ID, new GreaterConstraint(1));
+ if(config.grab(kP)) {
+ k = kP.getValue();
+ }
+ }
+
+ @Override
+ protected LDOF<O, D> makeInstance() {
+ return new LDOF<O, D>(distanceFunction, k);
+ }
+ }
+}
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