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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/trivial/TrivialAllNoise.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/trivial/TrivialAllNoise.java
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+package de.lmu.ifi.dbs.elki.algorithm.clustering.trivial;
+/*
+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.AbstractAlgorithm;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm;
+import de.lmu.ifi.dbs.elki.data.Cluster;
+import de.lmu.ifi.dbs.elki.data.Clustering;
+import de.lmu.ifi.dbs.elki.data.model.ClusterModel;
+import de.lmu.ifi.dbs.elki.data.model.Model;
+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.ids.DBIDs;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.logging.Logging;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
+
+/**
+ * Trivial pseudo-clustering that just considers all points to be noise.
+ * 
+ * Useful for evaluation and testing.
+ * 
+ * @author Erich Schubert
+ */
+@Title("Trivial all-noise clustering")
+@Description("Returns a 'trivial' clustering which just considers all points as noise points.")
+public class TrivialAllNoise extends AbstractAlgorithm<Clustering<Model>> implements ClusteringAlgorithm<Clustering<Model>> {
+  /**
+   * The logger for this class.
+   */
+  private static final Logging logger = Logging.getLogger(TrivialAllNoise.class);
+
+  /**
+   * Constructor, adhering to
+   * {@link de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable}
+   */
+  public TrivialAllNoise() {
+    super();
+  }
+
+  public Clustering<Model> run(Relation<?> relation) {
+    final DBIDs ids = relation.getDBIDs();
+    Clustering<Model> result = new Clustering<Model>("All-in-noise trivial Clustering", "allinnoise-clustering");
+    Cluster<Model> c = new Cluster<Model>(ids, true, ClusterModel.CLUSTER);
+    result.addCluster(c);
+    return result;
+  }
+
+  @Override
+  public TypeInformation[] getInputTypeRestriction() {
+    return TypeUtil.array(TypeUtil.ANY);
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return logger;
+  }
+}
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