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A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data

Zhang, Ke; Hutter, Marcus; Jin, Huidong

Description

Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel Local Distance-based Outlier Factor (LDOF) to measure the outlier-ness of objects in scattered datasets which addresses these issues. LDOF uses the relative location of an object to...[Show more]

dc.contributor.authorZhang, Ke
dc.contributor.authorHutter, Marcus
dc.contributor.authorJin, Huidong
dc.date.accessioned2015-08-26T05:39:36Z
dc.date.available2015-08-26T05:39:36Z
dc.identifier.isbn978-3-642-01306-5
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/1885/14967
dc.description.abstractDetecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel Local Distance-based Outlier Factor (LDOF) to measure the outlier-ness of objects in scattered datasets which addresses these issues. LDOF uses the relative location of an object to its neighbours to determine the degree to which the object deviates from its neighbourhood. We present theoretical bounds on LDOF’s false-detection probability. Experimentally, LDOF compares favorably to classical KNN and LOF based outlier detection. In particular it is less sensitive to parameter values.
dc.publisherSpringer Verlag
dc.relation.ispartofAdvances in knowledge discovery and data mining 13th Pacific-Asia Conference, PAKDD 2009, Bangkok, Thailand, April 27-30, 2009 ; proceedings
dc.rights© Springer-Verlag Berlin Heidelberg 2009. http://www.sherpa.ac.uk/romeo/issn/0302-9743/..."Author's post-print on any open access repository after 12 months after publication" from SHERPA/RoMEO site (as at 26/08/15)
dc.subjectlocal outlier
dc.subjectscattered data
dc.subjectk-distance
dc.subjectKNN
dc.subjectLOF
dc.subjectLDOF
dc.titleA New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
dc.typeConference paper
local.identifier.citationvolume5476
dc.date.issued2009
local.type.statusAccepted Version
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
local.bibliographicCitation.startpage813
local.bibliographicCitation.lastpage822
local.identifier.doi10.1007/978-3-642-01307-2_84
CollectionsANU Research Publications

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