See: Description
| Interface | Description |
|---|---|
| ComputeKNNOutlierScores.AlgRunner |
Run an algorithm for a given k.
|
| Class | Description |
|---|---|
| ComputeKNNOutlierScores<O extends NumberVector<?>,D extends NumberDistance<D,?>> |
Application that runs a series of kNN-based algorithms on a data set, for
building an ensemble in a second step.
|
| ComputeKNNOutlierScores.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> |
Parameterization class.
|
| GreedyEnsembleExperiment |
Class to load an outlier detection summary file, as produced by
ComputeKNNOutlierScores, and compute a naive ensemble for it. |
| GreedyEnsembleExperiment.Parameterizer |
Parameterization class.
|
| VisualizePairwiseGainMatrix |
Class to load an outlier detection summary file, as produced by
ComputeKNNOutlierScores, and compute a matrix with the pairwise
gains. |
| VisualizePairwiseGainMatrix.Parameterizer |
Parameterization class.
|
| Enum | Description |
|---|---|
| GreedyEnsembleExperiment.Distance |
Distance modes.
|
Greedy ensembles for outlier detection.
This package contains code that was used for the greedy ensemble experiment in
E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel
On Evaluation of Outlier Rankings and Outlier Scores
In Proceedings of the 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012.