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

Example 56 with Database

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

the class AnderbergHierarchicalClusteringTest method testMinimumVariance.

/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testMinimumVariance() {
    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, // 
    AnderbergHierarchicalClustering.class).with(AGNES.Parameterizer.LINKAGE_ID, // 
    MinimumVarianceLinkage.class).build().run(db);
    testFMeasure(db, clustering, 0.93866265);
    testClusterSizes(clustering, new int[] { 200, 211, 227 });
}

Example 57 with Database

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

the class AnderbergHierarchicalClusteringTest method testBetaVariance.

/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testBetaVariance() {
    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, // 
    AnderbergHierarchicalClustering.class).with(AGNES.Parameterizer.LINKAGE_ID, // 
    FlexibleBetaLinkage.class).with(FlexibleBetaLinkage.Parameterizer.BETA_ID, // 
    -.33).build().run(db);
    testFMeasure(db, clustering, 0.9277466);
    testClusterSizes(clustering, new int[] { 196, 200, 242 });
}

Example 58 with Database

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

the class AnderbergHierarchicalClusteringTest method testCompleteLink.

/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testCompleteLink() {
    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, // 
    AnderbergHierarchicalClustering.class).with(AGNES.Parameterizer.LINKAGE_ID, // 
    CompleteLinkage.class).build().run(db);
    testFMeasure(db, clustering, 0.938167802);
    testClusterSizes(clustering, new int[] { 200, 217, 221 });
}

Example 59 with Database

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

the class MiniMaxNNChainTest 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, // 
    MiniMaxNNChain.class).build().run(db);
    testFMeasure(db, clustering, 0.914592130);
    testClusterSizes(clustering, new int[] { 59, 112, 159 });
}

Example 60 with Database

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

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