Examples with ELKIBuilder de.lmu.ifi.dbs.elki.utilities.ELKIBuilder used on opensource projects

Example 6 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class MiniMaxTest method testMiniMax.

// TODO: add more data sets.
/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testMiniMax() {
    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
    Clustering<?> clustering = // 
    new ELKIBuilder<>(CutDendrogramByNumberOfClusters.class).with(CutDendrogramByNumberOfClusters.Parameterizer.MINCLUSTERS_ID, // 
    3).with(AbstractAlgorithm.ALGORITHM_ID, // 
    MiniMax.class).build().run(db);
    testFMeasure(db, clustering, 0.938662648);
    testClusterSizes(clustering, new int[] { 200, 211, 227 });
}

Example 7 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class MiniMaxTest method testMiniMax2.

/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testMiniMax2() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    Clustering<?> clustering = // 
    new ELKIBuilder<>(CutDendrogramByNumberOfClusters.class).with(CutDendrogramByNumberOfClusters.Parameterizer.MINCLUSTERS_ID, // 
    3).with(AbstractAlgorithm.ALGORITHM_ID, // 
    MiniMax.class).build().run(db);
    testFMeasure(db, clustering, 0.914592130);
    testClusterSizes(clustering, new int[] { 59, 112, 159 });
}

Example 8 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class CutDendrogramByHeightTest method testSLINKResults.

@Test
public void testSLINKResults() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    Clustering<?> clustering = // 
    new ELKIBuilder<>(CutDendrogramByHeight.class).with(CutDendrogramByHeight.Parameterizer.THRESHOLD_ID, // 
    0.14).with(AbstractAlgorithm.ALGORITHM_ID, // 
    SLINK.class).build().run(db);
    testFMeasure(db, clustering, 0.9474250948);
    testClusterSizes(clustering, new int[] { 1, 1, 1, 1, 1, 2, 3, 62, 104, 154 });
}

Example 9 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class HDBSCANHierarchyExtractionTest method testHDBSCANResults.

@Test
public void testHDBSCANResults() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    HDBSCANHierarchyExtraction slink = // 
    new ELKIBuilder<>(HDBSCANHierarchyExtraction.class).with(HDBSCANHierarchyExtraction.Parameterizer.MINCLUSTERSIZE_ID, // 
    50).with(AbstractAlgorithm.ALGORITHM_ID, // 
    HDBSCANLinearMemory.class).with(HDBSCANLinearMemory.Parameterizer.MIN_PTS_ID, // 
    20).build();
    testFMeasure(db, slink.run(db), 0.97218);
    testClusterSizes(slink.run(db), new int[] { 21, 54, 103, 152 });
}

Example 10 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class CLARANSTest method testCLARANSNoise.

@Test
public void testCLARANSNoise() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    Clustering<MedoidModel> result = // 
    new ELKIBuilder<CLARANS<DoubleVector>>(CLARANS.class).with(KMeans.K_ID, // 
    3).with(CLARANS.Parameterizer.RANDOM_ID, // 
    0).with(CLARANS.Parameterizer.NEIGHBORS_ID, // 
    .1).with(CLARANS.Parameterizer.RESTARTS_ID, // 
    5).build().run(db);
    testFMeasure(db, result, 0.913858);
    testClusterSizes(result, new int[] { 57, 115, 158 });
}