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package de.lmu.ifi.dbs.elki.algorithm.clustering.correlation;
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.algorithm.clustering.AbstractProjectedDBSCAN;
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;
/**
* Perform a full 4C run, and compare the result with a clustering derived from
* the data set labels. This test ensures that 4C performance doesn't
* unexpectedly drop on this data set (and also ensures that the algorithms
* work, as a side effect).
*
* @author Erich Schubert
* @author Katharina Rausch
*/
public class TestFourCResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
/**
* Run 4F with fixed parameters and compare the result to a golden standard.
*
* @throws ParameterException on errors.
*/
@Test
public void testFourCResults() {
Database db = makeSimpleDatabase(UNITTEST + "hierarchical-3d2d1d.csv", 600);
// Setup 4C
ListParameterization params = new ListParameterization();
params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 0.30);
params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 20);
params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 5);
FourC<DoubleVector> fourc = ClassGenericsUtil.parameterizeOrAbort(FourC.class, params);
testParameterizationOk(params);
// run 4C on database
Clustering<Model> result = fourc.run(db);
testFMeasureHierarchical(db, result, 0.79467);
testClusterSizes(result, new int[] { 5, 595 });
}
/**
* Run ERiC with fixed parameters and compare the result to a golden standard.
*
* @throws ParameterException on errors.
*/
@Test
public void testFourCOverlap() {
Database db = makeSimpleDatabase(UNITTEST + "correlation-overlap-3-5d.ascii", 650);
// Setup algorithm
ListParameterization params = new ListParameterization();
// 4C
params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 1.2);
params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 5);
params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 3);
FourC<DoubleVector> fourc = ClassGenericsUtil.parameterizeOrAbort(FourC.class, params);
testParameterizationOk(params);
// run 4C on database
Clustering<Model> result = fourc.run(db);
testFMeasure(db, result, 0.48305405);
testClusterSizes(result, new int[] { 65, 70, 515 });
}
}
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