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package de.lmu.ifi.dbs.elki.algorithm.clustering.subspace;
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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