1 files changed, 96 insertions, 0 deletions
diff --git a/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/DBSCANTest.java b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/DBSCANTest.java
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+++ b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/DBSCANTest.java
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+package de.lmu.ifi.dbs.elki.algorithm.clustering;
+
+/*
+ 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 org.junit.Test;
+
+import de.lmu.ifi.dbs.elki.JUnit4Test;
+import de.lmu.ifi.dbs.elki.algorithm.AbstractSimpleAlgorithmTest;
+import de.lmu.ifi.dbs.elki.data.Clustering;
+import de.lmu.ifi.dbs.elki.data.DoubleVector;
+import de.lmu.ifi.dbs.elki.data.model.Model;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.ParameterException;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
+
+/**
+ * Performs a full DBSCAN run, and compares the result with a clustering derived
+ * from the data set labels. This test ensures that DBSCAN performance doesn't
+ * unexpectedly drop on this data set (and also ensures that the algorithms
+ * work, as a side effect).
+ * 
+ * @author Elke Achtert
+ * @author Erich Schubert
+ * @author Katharina Rausch
+ * @since 0.3
+ */
+public class DBSCANTest extends AbstractSimpleAlgorithmTest implements JUnit4Test {
+  /**
+   * Run DBSCAN with fixed parameters and compare the result to a golden
+   * standard.
+   * 
+   * @throws ParameterException
+   */
+  @Test
+  public void testDBSCANResults() {
+    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
+
+    // setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(DBSCAN.Parameterizer.EPSILON_ID, 0.04);
+    params.addParameter(DBSCAN.Parameterizer.MINPTS_ID, 20);
+    DBSCAN<DoubleVector> dbscan = ClassGenericsUtil.parameterizeOrAbort(DBSCAN.class, params);
+    testParameterizationOk(params);
+
+    // run DBSCAN on database
+    Clustering<Model> result = dbscan.run(db);
+
+    testFMeasure(db, result, 0.996413);
+    testClusterSizes(result, new int[] { 29, 50, 101, 150 });
+  }
+
+  /**
+   * Run DBSCAN with fixed parameters and compare the result to a golden
+   * standard.
+   * 
+   * @throws ParameterException
+   */
+  @Test
+  public void testDBSCANOnSingleLinkDataset() {
+    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(DBSCAN.Parameterizer.EPSILON_ID, 11.5);
+    params.addParameter(DBSCAN.Parameterizer.MINPTS_ID, 120);
+    DBSCAN<DoubleVector> dbscan = ClassGenericsUtil.parameterizeOrAbort(DBSCAN.class, params);
+    testParameterizationOk(params);
+
+    // run DBSCAN on database
+    Clustering<Model> result = dbscan.run(db);
+    testFMeasure(db, result, 0.954382);
+    testClusterSizes(result, new int[] { 11, 200, 203, 224 });
+  }
+}
+\ No newline at end of file