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package de.lmu.ifi.dbs.elki.algorithm.clustering.subspace;
/*
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 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 PROCLUS run, and compares the result with a clustering
* derived from the data set labels. This test ensures that PROCLUS performance
* doesn't unexpectedly drop on this data set (and also ensures that the
* algorithms work, as a side effect).
*
* @author Elke Achtert
* @author Katharina Rausch
* @author Erich Schubert
*/
public class TestPROCLUSResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
/**
* Run PROCLUS with fixed parameters and compare the result to a golden
* standard.
*
* @throws ParameterException
*/
@Test
public void testPROCLUSResults() {
Database db = makeSimpleDatabase(UNITTEST + "subspace-simple.csv", 600);
ListParameterization params = new ListParameterization();
params.addParameter(PROCLUS.L_ID, 1);
params.addParameter(PROCLUS.K_ID, 4);
params.addParameter(PROCLUS.SEED_ID, 1);
// setup algorithm
PROCLUS<DoubleVector> proclus = ClassGenericsUtil.parameterizeOrAbort(PROCLUS.class, params);
testParameterizationOk(params);
// run PROCLUS on database
Clustering<Model> result = proclus.run(db);
testFMeasure(db, result, 0.68932);
testClusterSizes(result, new int[] { 78, 93, 203, 226 });
}
/**
* Run PROCLUS with fixed parameters and compare the result to a golden
* standard.
*
* @throws ParameterException
*/
@Test
public void testPROCLUSSubspaceOverlapping() {
Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850);
// Setup algorithm
ListParameterization params = new ListParameterization();
params.addParameter(PROCLUS.L_ID, 2);
params.addParameter(PROCLUS.K_ID, 3);
params.addParameter(PROCLUS.SEED_ID, 2);
PROCLUS<DoubleVector> proclus = ClassGenericsUtil.parameterizeOrAbort(PROCLUS.class, params);
testParameterizationOk(params);
// run PROCLUS on database
Clustering<Model> result = proclus.run(db);
testFMeasure(db, result, 0.9673718);
testClusterSizes(result, new int[] { 150, 289, 411 });
}
}
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