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diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/quality/AkaikeInformationCriterion.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/quality/AkaikeInformationCriterion.java
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+package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2015
+ 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.data.Clustering;
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.model.MeanModel;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+
+/**
+ * Akaike Information Criterion (AIC).
+ *
+ * Reference:
+ * <p>
+ * H. Akaike<br />
+ * On entropy maximization principle<br />
+ * Application of statistics, 1977, North-Holland
+ * </p>
+ *
+ * The use for k-means was popularized by:
+ * <p>
+ * D. Pelleg, A. Moore:<br />
+ * X-means: Extending K-means with Efficient Estimation on the Number of
+ * Clusters<br />
+ * In: Proceedings of the 17th International Conference on Machine Learning
+ * (ICML 2000)
+ * </p>
+ *
+ * @author Tibor Goldschwendt
+ * @author Erich Schubert
+ */
+@Reference(authors = "H. Akaike", //
+title = "On entropy maximization principle", //
+booktitle = "Application of statistics, 1977, North-Holland")
+public class AkaikeInformationCriterion extends AbstractKMeansQualityMeasure<NumberVector> {
+  @Override
+  public <V extends NumberVector> double quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) {
+    return logLikelihood(relation, clustering, distanceFunction) - numberOfFreeParameters(relation, clustering);
+  }
+
+  @Override
+  public boolean ascending() {
+    return true;
+  }
+
+  @Override
+  public boolean isBetter(double currentCost, double bestCost) {
+    // Careful: bestCost may be NaN!
+    return !(currentCost <= bestCost);
+  }
+}