Examples with MedoidModel de.lmu.ifi.dbs.elki.data.model.MedoidModel used on opensource projects

Example 1 with MedoidModel

use of de.lmu.ifi.dbs.elki.data.model.MedoidModel 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 });
}

Example 2 with MedoidModel

use of de.lmu.ifi.dbs.elki.data.model.MedoidModel in project elki by elki-project.

the class CLARATest method testCLARA.

/**
 * Run CLARA with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testCLARA() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
    Clustering<MedoidModel> result = // 
    new ELKIBuilder<CLARA<DoubleVector>>(CLARA.class).with(KMeans.K_ID, // 
    5).with(CLARA.Parameterizer.RANDOM_ID, // 
    1).with(CLARA.Parameterizer.NUMSAMPLES_ID, // 
    2).with(CLARA.Parameterizer.SAMPLESIZE_ID, // 
    50).build().run(db);
    testFMeasure(db, result, 0.998005);
    testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 });
}

Example 3 with MedoidModel

use of de.lmu.ifi.dbs.elki.data.model.MedoidModel in project elki by elki-project.

the class KMedoidsEMTest method testKMedoidsEM.

/**
 * Run KMedoidsEM with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testKMedoidsEM() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
    Clustering<MedoidModel> result = // 
    new ELKIBuilder<KMedoidsEM<DoubleVector>>(KMedoidsEM.class).with(KMeans.K_ID, // 
    5).with(KMeans.INIT_ID, // 
    PAMInitialMeans.class).build().run(db);
    testFMeasure(db, result, 0.998005);
    testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 });
}

Example 4 with MedoidModel

use of de.lmu.ifi.dbs.elki.data.model.MedoidModel in project elki by elki-project.

the class AffinityPropagationClusteringAlgorithmTest method testAffinityPropagationClusteringAlgorithmOnSingleLinkDataset.

/**
 * Run AffinityPropagationClusteringAlgorithm with fixed parameters and
 * compare the result to a golden standard.
 */
@Test
public void testAffinityPropagationClusteringAlgorithmOnSingleLinkDataset() {
    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
    Clustering<MedoidModel> result = // 
    new ELKIBuilder<AffinityPropagationClusteringAlgorithm<DoubleVector>>(AffinityPropagationClusteringAlgorithm.class).build().run(db);
    testFMeasure(db, result, 0.351689882);
    testClusterSizes(result, new int[] { 24, 27, 29, 34, 36, 36, 37, 38, 41, 43, 43, 44, 46, 47, 56, 57 });
}

Example 5 with MedoidModel

use of de.lmu.ifi.dbs.elki.data.model.MedoidModel in project elki by elki-project.

the class CLARANSTest method testCLARANS.

@Test
public void testCLARANS() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
    Clustering<MedoidModel> result = // 
    new ELKIBuilder<CLARANS<DoubleVector>>(CLARANS.class).with(KMeans.K_ID, // 
    5).with(CLARANS.Parameterizer.RANDOM_ID, // 
    0).with(CLARANS.Parameterizer.NEIGHBORS_ID, // 
    10).with(CLARANS.Parameterizer.RESTARTS_ID, // 
    5).build().run(db);
    testFMeasure(db, result, 0.996);
    testClusterSizes(result, new int[] { 198, 200, 200, 200, 202 });
}