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refineResult(Relation<V>, List<Vector>, List<ModifiableDBIDs>, WritableDataStore<SameSizeKMeansAlgorithm<V>.Meta>, ArrayModifiableDBIDs) - Method in class tutorial.clustering.SameSizeKMeansAlgorithm
Perform k-means style iterations to improve the clustering result.
run(Database, Relation<O>) - Method in class tutorial.clustering.NaiveAgglomerativeHierarchicalClustering1
Run the algorithm
run(Database, Relation<O>) - Method in class tutorial.clustering.NaiveAgglomerativeHierarchicalClustering2
Run the algorithm
run(Database, Relation<O>) - Method in class tutorial.clustering.NaiveAgglomerativeHierarchicalClustering3
Run the algorithm
run(Database, Relation<O>) - Method in class tutorial.clustering.NaiveAgglomerativeHierarchicalClustering4
Run the algorithm
run(Database, Relation<V>) - Method in class tutorial.clustering.SameSizeKMeansAlgorithm
Run k-means with cluster size constraints.
run(Database, Relation<O>) - Method in class tutorial.outlier.DistanceStddevOutlier
Run the outlier detection algorithm
run(Database, Relation<O>) - Method in class tutorial.outlier.ODIN
Run the ODIN algorithm Tutorial note: the signature of this method depends on the types that we requested in the ODIN.getInputTypeRestriction() method.
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ELKI Version 0.7.1

Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.