An Effective Approach to Outlier Detection Based on Centrality and Centre-Proximity
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bae, Duck-Ho | - |
dc.contributor.author | Jeong, Seo | - |
dc.contributor.author | Hong, Jiwon | - |
dc.contributor.author | Lee, Minsoo | - |
dc.contributor.author | Ivanovic, Mirjana | - |
dc.contributor.author | Savic, Milos | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2022-07-08T03:29:59Z | - |
dc.date.available | 2022-07-08T03:29:59Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 0868-4952 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145718 | - |
dc.description.abstract | In data mining research, outliers usually represent extreme values that deviate from other observations on data. The significant issue of existing outlier detection methods is that they only consider the object itself not taking its neighbouring objects into account to extract location features. In this paper, we propose an innovative approach to this issue. First, we propose the notions of centrality and centre-proximity for determining the degree of outlierness considering the distribution of all objects. We also propose a novel graph-based algorithm for outlier detection based on the notions. The algorithm solves the problems of existing methods, i.e. the problems of local density, micro-cluster, and fringe objects. We performed extensive experiments in order to confirm the effectiveness and efficiency of our proposed method. The obtained experimental results showed that the proposed method uncovers outliers successfully, and outperforms previous outlier detection methods. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | INST MATHEMATICS & INFORMATICS | - |
dc.title | An Effective Approach to Outlier Detection Based on Centrality and Centre-Proximity | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.15388/20-INFOR413 | - |
dc.identifier.scopusid | 2-s2.0-85092920159 | - |
dc.identifier.wosid | 000575384000001 | - |
dc.identifier.bibliographicCitation | INFORMATICA, v.31, no.3, pp.435 - 458 | - |
dc.relation.isPartOf | INFORMATICA | - |
dc.citation.title | INFORMATICA | - |
dc.citation.volume | 31 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 435 | - |
dc.citation.endPage | 458 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Applied | - |
dc.subject.keywordPlus | ANOMALY DETECTION | - |
dc.subject.keywordPlus | ALGORITHMS | - |
dc.subject.keywordAuthor | graph-based outlier detection | - |
dc.subject.keywordAuthor | centrality | - |
dc.subject.keywordAuthor | centre-proximity | - |
dc.identifier.url | https://informatica.vu.lt/journal/INFORMATICA/article/1177/info | - |
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.