Outlier detection using centrality and center-proximity
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bae, Duck-Ho | - |
dc.contributor.author | Jeong, Seo | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Lee, Minsoo | - |
dc.date.accessioned | 2022-07-16T12:54:19Z | - |
dc.date.available | 2022-07-16T12:54:19Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2012-11 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/164269 | - |
dc.description.abstract | An outlier is an object that is considerably dissimilar with the remainder of the dataset. In this paper, we first propose the notion of centrality and center-proximity as novel outlierness measures which can be considered to represent the characteristics of all of the objects in the dataset. We then propose a graph-based outlier detection method which can solve the problems of local density, micro-cluster, and fringe objects. Finally, through extensive experiments, we show the effectiveness of the proposed method. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinary, Inc. | - |
dc.title | Outlier detection using centrality and center-proximity | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/2396761.2398613 | - |
dc.identifier.scopusid | 2-s2.0-84871089735 | - |
dc.identifier.bibliographicCitation | ACM International Conference Proceeding Series, pp.2251 - 2254 | - |
dc.relation.isPartOf | ACM International Conference Proceeding Series | - |
dc.citation.title | ACM International Conference Proceeding Series | - |
dc.citation.startPage | 2251 | - |
dc.citation.endPage | 2254 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | center-proximity | - |
dc.subject.keywordPlus | centrality | - |
dc.subject.keywordPlus | Data sets | - |
dc.subject.keywordPlus | Graph-based | - |
dc.subject.keywordPlus | Local density | - |
dc.subject.keywordPlus | Outlier Detection | - |
dc.subject.keywordPlus | Graphic methods | - |
dc.subject.keywordPlus | Knowledge management | - |
dc.subject.keywordPlus | Statistics | - |
dc.subject.keywordAuthor | center-proximity | - |
dc.subject.keywordAuthor | centrality | - |
dc.subject.keywordAuthor | graph-based outlier detection | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/2396761.2398613 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.