Outlier detection using ball descriptions with adjustable metric

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Outlier detection using ball descriptions with adjustable metric

Conference Paper (2006)

Author(s)

David M. J. Tax (TU Delft - Multimedia Computing)

P Juszczak (TU Delft - Multimedia Computing)

E.M. Pekalska (TU Delft - Multimedia Computing)

Bob Duin (TU Delft - Multimedia Computing)

Multimedia Computing

CWTS 0.75 <= JFIS < 2.00 Techniek Conference contrib. refereed Wiskunde en Informatica Technische Wiskunde en Informatica

To reference this document use:

https://resolver.tudelft.nl/uuid:aed5632a-3036-481e-812c-319d49c495f8

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Publication Year

2006

Multimedia Computing

Pages (from-to)

587-595

ISBN (print)

3-540-37236-9

Abstract

Sometimesnoveloroutlierdatahastobedetected.Theoutliersmayindicatesomeinterestingrareevent,ortheyshouldbedisregardedbecausetheycannotbereliablyprocessedfurther.Intheidealcasethattheobjectsarerepresentedbyverygoodfeatures,thegenuinedataformsacompactclusterandagoodoutliermeasureisthedistancetotheclustercenter.Thispaperproposesthreenewformulationsto¿ndagoodclustercentertogetherwithanoptimizedp-distancemeasure.Experimentsshowthatforsomerealworlddatasetsverygoodclassi¿cationresultsareobtainedandthat,morespeci¿cally,the1-distanceisparticularlysuitedfordatasetscontainingdiscretefeaturevalues.

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