Detecting pattern-based outliers

Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0167-8655(03)00165-X

DC Field	Value
dc.title	Detecting pattern-based outliers
dc.contributor.author	Hu, T.
dc.contributor.author	Sung, S.Y.
dc.date.accessioned	2013-07-04T07:30:46Z
dc.date.available	2013-07-04T07:30:46Z
dc.date.issued	2003
dc.identifier.citation	Hu, T., Sung, S.Y. (2003). Detecting pattern-based outliers. Pattern Recognition Letters 24 (16) : 3059-3068. ScholarBank@NUS Repository. https://doi.org/10.1016/S0167-8655(03)00165-X
dc.identifier.issn	01678655
dc.identifier.uri	http://scholarbank.nus.edu.sg/handle/10635/38959
dc.description.abstract	Outlier detection targets those exceptional data that deviate from the general pattern. Besides high density clustering, there is another pattern called low density regularity. Thus, there are two types of outliers w.r.t. them. We propose two techniques: one to identify the two patterns and the other to detect the corresponding outliers. © 2003 Elsevier B.V. All rights reserved.
dc.description.uri	http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0167-8655(03)00165-X
dc.source	Scopus
dc.subject	Clustering
dc.subject	Complete spatial randomness
dc.subject	Outlier detection
dc.subject	Regular spacing
dc.type	Article
dc.contributor.department	COMPUTER SCIENCE
dc.description.doi	10.1016/S0167-8655(03)00165-X
dc.description.sourcetitle	Pattern Recognition Letters
dc.description.volume	24
dc.description.issue	16
dc.description.page	3059-3068
dc.identifier.isiut	000186170700015
Appears in Collections:	Staff Publications

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