Outlier Detection Using k-Nearest Neighbour Graph

Ville Hautamäki, Ismo Kärkkäinen, P. Fränti
{"title":"Outlier Detection Using k-Nearest Neighbour Graph","authors":"Ville Hautamäki, Ismo Kärkkäinen, P. Fränti","doi":"10.1109/ICPR.2004.1334558","DOIUrl":null,"url":null,"abstract":"We present an outlier detection using indegree number (ODIN) algorithm that utilizes k-nearest neighbour graph. Improvements to existing kNN distance-based method are also proposed. We compare the methods with real and synthetic datasets. The results show that the proposed method achieves reasonable results with synthetic data and outperforms compared methods with real data sets with small number of observations.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"52 1","pages":"430-433"},"PeriodicalIF":0.0000,"publicationDate":"2004-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"339","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2004.1334558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 339

Abstract

We present an outlier detection using indegree number (ODIN) algorithm that utilizes k-nearest neighbour graph. Improvements to existing kNN distance-based method are also proposed. We compare the methods with real and synthetic datasets. The results show that the proposed method achieves reasonable results with synthetic data and outperforms compared methods with real data sets with small number of observations.
基于k近邻图的离群点检测
提出了一种利用k近邻图的度数(ODIN)算法进行离群值检测的方法。对现有的基于kNN距离的方法进行了改进。我们将这些方法与真实数据集和合成数据集进行了比较。结果表明,该方法在合成数据上取得了合理的结果,在少量观测值的真实数据集上优于对比方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
3.70
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信