Package elki.outlier.clustering
Class KMeansMinusMinusOutlierDetection
- java.lang.Object
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- elki.outlier.clustering.NoiseAsOutliers
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- elki.outlier.clustering.KMeansMinusMinusOutlierDetection
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- All Implemented Interfaces:
Algorithm
,OutlierAlgorithm
@Title("K-Means--") @Reference(authors="S. Chawla, A. Gionis", title="k-means--: A Unified Approach to Clustering and Outlier Detection", booktitle="Proc. 13th SIAM Int. Conf. on Data Mining (SDM 2013)", url="https://doi.org/10.1137/1.9781611972832.21", bibkey="DBLP:conf/sdm/ChawlaG13") public class KMeansMinusMinusOutlierDetection extends NoiseAsOutliers
k-means--: A Unified Approach to Clustering and Outlier Detection.This implementation assigns the outlier label to all points that were identified as noise according to the
KMeans--
algorithm.Reference:
S. Chawla, A. Gionis
k-means--: A Unified Approach to Clustering and Outlier Detection
Proc. 13th SIAM Int. Conf. on Data Mining (SDM 2013)- Since:
- 0.8.0
- Author:
- Braulio V.S. Vinces (ELKIfication)
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
KMeansMinusMinusOutlierDetection.Par
Parameterizer.-
Nested classes/interfaces inherited from interface elki.Algorithm
Algorithm.Utils
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Field Summary
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Fields inherited from class elki.outlier.clustering.NoiseAsOutliers
clustering
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Constructor Summary
Constructors Constructor Description KMeansMinusMinusOutlierDetection(KMeansMinusMinus<?> kmeansminusminus)
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description OutlierResult
autorun(Database database)
Try to auto-run the algorithm on a database by calling a method calledrun
, with an optionalDatabase
first, and with data relations as specified byAlgorithm.getInputTypeRestriction()
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Methods inherited from class elki.outlier.clustering.NoiseAsOutliers
getInputTypeRestriction
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Constructor Detail
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KMeansMinusMinusOutlierDetection
public KMeansMinusMinusOutlierDetection(KMeansMinusMinus<?> kmeansminusminus)
Constructor.- Parameters:
kmeansminusminus
- Clustering algorithm
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Method Detail
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autorun
public OutlierResult autorun(Database database)
Description copied from interface:Algorithm
Try to auto-run the algorithm on a database by calling a method calledrun
, with an optionalDatabase
first, and with data relations as specified byAlgorithm.getInputTypeRestriction()
.- Specified by:
autorun
in interfaceAlgorithm
- Specified by:
autorun
in interfaceOutlierAlgorithm
- Overrides:
autorun
in classNoiseAsOutliers
- Parameters:
database
- the database to run the algorithm on- Returns:
- the Result computed by this algorithm
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