基于k近邻图的离群点检测

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2004-08-23 DOI:10.1109/ICPR.2004.1334558

Ville Hautamäki, Ismo Kärkkäinen, P. Fränti

{"title":"基于k近邻图的离群点检测","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":"{\"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}","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

摘要

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。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Outlier Detection Using k-Nearest Neighbour Graph

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊