Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김상욱 | - |
dc.date.accessioned | 2022-08-02T06:32:54Z | - |
dc.date.available | 2022-08-02T06:32:54Z | - |
dc.date.issued | 2020-10 | - |
dc.identifier.citation | INFORMATICA, v. 31, no. 3, page. 435-458 | en_US |
dc.identifier.issn | 0868-4952 | - |
dc.identifier.issn | 1822-8844 | - |
dc.identifier.uri | https://informatica.vu.lt/journal/INFORMATICA/article/1177/info | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/172011 | - |
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. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Ministry of Science and ICT (MSIT) (No. NRF-2020R1A2B5B03001960) and also by the Next-Generation Information Computing Development Program through the NRF funded by the MSIT (No. NRF-2017M3C4A7069440 and No. NRF2017M3C4A7083678). | en_US |
dc.language.iso | en | en_US |
dc.publisher | INST MATHEMATICS & INFORMATICS | en_US |
dc.subject | graph-based outlier detection | en_US |
dc.subject | centrality | en_US |
dc.subject | centre-proximity | en_US |
dc.title | An Effective Approach to Outlier Detection Based on Centrality and Centre-Proximity | en_US |
dc.type | Article | en_US |
dc.relation.no | 3 | - |
dc.relation.volume | 31 | - |
dc.identifier.doi | 10.15388/20-INFOR413 | - |
dc.relation.page | 435-458 | - |
dc.relation.journal | INFORMATICA | - |
dc.contributor.googleauthor | Bae, Duck-Ho | - |
dc.contributor.googleauthor | Jeong, Seo | - |
dc.contributor.googleauthor | Hong, Jiwon | - |
dc.contributor.googleauthor | Lee, Minsoo | - |
dc.contributor.googleauthor | Ivanovic, Mirjana | - |
dc.contributor.googleauthor | Savic, Milos | - |
dc.contributor.googleauthor | Kim, Sang-Wook | - |
dc.relation.code | 2020045527 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | SCHOOL OF COMPUTER SCIENCE | - |
dc.identifier.pid | wook | - |
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