Examples with Database de.lmu.ifi.dbs.elki.database.Database used on opensource projects

Example 11 with Database

use of de.lmu.ifi.dbs.elki.database.Database in project elki by elki-project.

the class MiniMaxAnderbergTest 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, // 
    MiniMaxAnderberg.class).build().run(db);
    testFMeasure(db, clustering, 0.938662648);
    testClusterSizes(clustering, new int[] { 200, 211, 227 });
}

Example 12 with Database

use of de.lmu.ifi.dbs.elki.database.Database 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 13 with Database

use of de.lmu.ifi.dbs.elki.database.Database 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 14 with Database

use of de.lmu.ifi.dbs.elki.database.Database in project elki by elki-project.

the class SLINKHDBSCANLinearMemoryTest method testHDBSCAN.

// TODO: add more data sets.
/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testHDBSCAN() {
    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, // 
    SLINKHDBSCANLinearMemory.class).with(SLINKHDBSCANLinearMemory.Parameterizer.MIN_PTS_ID, // 
    20).build().run(db);
    testFMeasure(db, clustering, 0.686953412);
    testClusterSizes(clustering, new int[] { 1, 200, 437 });
}

Example 15 with Database

use of de.lmu.ifi.dbs.elki.database.Database in project elki by elki-project.

the class BIRCHLeafClusteringTest method testCentroidManhattan.

@Test
public void testCentroidManhattan() {
    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
    Clustering<?> clustering = // 
    new ELKIBuilder<>(BIRCHLeafClustering.class).with(CFTree.Factory.Parameterizer.DISTANCE_ID, // d3
    CentroidManhattanDistance.class).with(CFTree.Factory.Parameterizer.ABSORPTION_ID, // 
    DiameterCriterion.class).with(CFTree.Factory.Parameterizer.MAXLEAVES_ID, // 
    4).build().run(db);
    testFMeasure(db, clustering, 0.92236);
    testClusterSizes(clustering, new int[] { 83, 154, 200, 201 });
}