Package elki.outlier.clustering

Class KMeansMinusMinusOutlierDetection

java.lang.Object

elki.outlier.clustering.NoiseAsOutliers

elki.outlier.clustering.KMeansMinusMinusOutlierDetection

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)

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

Field Summary

Fields inherited from class elki.outlier.clustering.NoiseAsOutliers

clustering

Constructor Summary

Constructors

Constructor	Description
KMeansMinusMinusOutlierDetection(KMeansMinusMinus<?> kmeansminusminus)	Constructor.

Method Summary

Modifier and Type	Method	Description
OutlierResult	autorun(Database database)	Try to auto-run the algorithm on a database by calling a method called run, with an optional Database first, and with data relations as specified by Algorithm.getInputTypeRestriction().

Methods inherited from class elki.outlier.clustering.NoiseAsOutliers

getInputTypeRestriction

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

KMeansMinusMinusOutlierDetection

public KMeansMinusMinusOutlierDetection(KMeansMinusMinus<?> kmeansminusminus)

Constructor.

Parameters:

kmeansminusminus - Clustering algorithm

Method Detail

autorun

public OutlierResult autorun(Database database)

Description copied from interface: Algorithm

Try to auto-run the algorithm on a database by calling a method called run, with an optional Database first, and with data relations as specified by Algorithm.getInputTypeRestriction().

Specified by:

autorun in interface Algorithm

Specified by:

autorun in interface OutlierAlgorithm

Overrides:

autorun in class NoiseAsOutliers

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm