Package | Description |
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de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
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de.lmu.ifi.dbs.elki.evaluation.clustering.internal |
Internal evaluation measures for clusterings.
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Class and Description |
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NoiseHandling
Options for handling noise in internal measures.
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Class and Description |
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EvaluateCIndex
Compute the C-index of a data set.
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EvaluateConcordantPairs
Compute the Gamma Criterion of a data set.
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EvaluateDaviesBouldin
Compute the Davies-Bouldin index of a data set.
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EvaluatePBMIndex
Compute the PBM of a data set
Reference:
M.
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EvaluateSilhouette
Compute the silhouette of a data set.
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EvaluateSimplifiedSilhouette
Compute the simplified silhouette of a data set.
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EvaluateSquaredErrors
Evaluate a clustering by reporting the squared errors (SSE, SSQ), as used by
k-means.
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EvaluateVarianceRatioCriteria
Compute the Variance Ratio Criteria of a data set.
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NoiseHandling
Options for handling noise in internal measures.
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Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.