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Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/KNNWeightOutlier.java')
| -rw-r--r-- | src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/KNNWeightOutlier.java | 205 |
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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/KNNWeightOutlier.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/KNNWeightOutlier.java new file mode 100644 index 00000000..b09f7480 --- /dev/null +++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/KNNWeightOutlier.java @@ -0,0 +1,205 @@ +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.algorithm.AbstractDistanceBasedAlgorithm;
+import de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm;
+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.DBIDUtil;
+import de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter;
+import de.lmu.ifi.dbs.elki.database.ids.KNNList;
+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.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.distance.distancefunction.DistanceFunction;
+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.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.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;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
+
+/**
+ * Outlier Detection based on the accumulated distances of a point to its k
+ * nearest neighbors.
+ *
+ * As in the original publication (as far as we could tell from the pseudocode
+ * included), the current point is not included in the nearest neighbors (see
+ * figures in the publication). This matches the intuition common in nearest
+ * neighbor classification, where the evaluated instances are not part of the
+ * training set; but it contrasts to the pseudocode of the kNN outlier method
+ * and the database interpretation (which returns all objects stored in the
+ * database).
+ *
+ * Furthermore, we report the sum of the k distances (called "weight" in the
+ * original publication). Other implementations may return the average distance
+ * instead, and therefore yield different results.
+ *
+ * Reference:
+ * <p>
+ * F. Angiulli, C. Pizzuti:<br />
+ * Fast Outlier Detection in High Dimensional Spaces.<br />
+ * In: Proc. European Conference on Principles of Knowledge Discovery and Data
+ * Mining (PKDD'02), Helsinki, Finland, 2002.
+ * </p>
+ *
+ * @author Lisa Reichert
+ *
+ * @apiviz.has KNNQuery
+ *
+ * @param <O> the type of DatabaseObjects handled by this Algorithm
+ */
+@Title("KNNWeight outlier detection")
+@Description("Outlier detection based on the sum of distances of an object to its k nearest neighbors.")
+@Reference(authors = "F. Angiulli, C. Pizzuti", //
+title = "Fast Outlier Detection in High Dimensional Spaces", //
+booktitle = "Proc. European Conference on Principles of Knowledge Discovery and Data Mining (PKDD'02), Helsinki, Finland, 2002", //
+url = "http://dx.doi.org/10.1007/3-540-45681-3_2")
+@Alias({ "de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier", "knnw" })
+public class KNNWeightOutlier<O> extends AbstractDistanceBasedAlgorithm<O, OutlierResult> implements OutlierAlgorithm {
+ /**
+ * The logger for this class.
+ */
+ private static final Logging LOG = Logging.getLogger(KNNWeightOutlier.class);
+
+ /**
+ * Holds the number of nearest neighbors to query (including query point!)
+ */
+ private int k;
+
+ /**
+ * Constructor with parameters.
+ *
+ * @param distanceFunction Distance function
+ * @param k k Parameter (not including query point!)
+ */
+ public KNNWeightOutlier(DistanceFunction<? super O> distanceFunction, int k) {
+ super(distanceFunction);
+ this.k = k;
+ }
+
+ /**
+ * Runs the algorithm in the timed evaluation part.
+ *
+ * @param database Database context
+ * @param relation Data relation
+ */
+ public OutlierResult run(Database database, Relation<O> relation) {
+ final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
+ KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, k + 1);
+
+ FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Compute kNN weights.", relation.size(), LOG) : null;
+
+ DoubleMinMax minmax = new DoubleMinMax();
+ WritableDoubleDataStore knnw_score = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ final KNNList knn = knnQuery.getKNNForDBID(iditer, k + 1);
+ double skn = 0; // sum of the distances to the k nearest neighbors
+ int i = 0; // number of neighbors so far
+ for(DoubleDBIDListIter neighbor = knn.iter(); i < k && neighbor.valid(); neighbor.advance()) {
+ if(DBIDUtil.equal(iditer, neighbor)) {
+ continue;
+ }
+ skn += neighbor.doubleValue();
+ ++i;
+ }
+ if(i < k) {
+ // Less than k neighbors found
+ // Approximative index, or k > data set size!
+ skn = Double.POSITIVE_INFINITY;
+ }
+ knnw_score.putDouble(iditer, skn);
+ minmax.put(skn);
+
+ LOG.incrementProcessed(prog);
+ }
+ LOG.ensureCompleted(prog);
+
+ DoubleRelation res = new MaterializedDoubleRelation("kNN weight Outlier Score", "knnw-outlier", knnw_score, relation.getDBIDs());
+ OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0., Double.POSITIVE_INFINITY, 0.);
+ return new OutlierResult(meta, res);
+ }
+
+ @Override
+ public TypeInformation[] getInputTypeRestriction() {
+ return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
+ }
+
+ @Override
+ protected Logging getLogger() {
+ return LOG;
+ }
+
+ /**
+ * Parameterization class.
+ *
+ * @author Erich Schubert
+ *
+ * @apiviz.exclude
+ */
+ public static class Parameterizer<O> extends AbstractDistanceBasedAlgorithm.Parameterizer<O> {
+ /**
+ * Parameter to specify the k nearest neighbor.
+ */
+ public static final OptionID K_ID = new OptionID("knnwod.k", //
+ "The k nearest neighbor, excluding the query point "//
+ + "(i.e. query point is the 0-nearest-neighbor)");
+
+ /**
+ * k parameter
+ */
+ protected int k = 0;
+
+ @Override
+ protected void makeOptions(Parameterization config) {
+ super.makeOptions(config);
+ final IntParameter kP = new IntParameter(K_ID) //
+ .addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
+ if(config.grab(kP)) {
+ k = kP.getValue();
+ }
+ }
+
+ @Override
+ protected KNNWeightOutlier<O> makeInstance() {
+ return new KNNWeightOutlier<>(distanceFunction, k);
+ }
+ }
+}
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