A Relative Kernel-density Based Outlier Detection Algorithm

A. Wahid, A. Rao, Koushik Deb
{"title":"A Relative Kernel-density Based Outlier Detection Algorithm","authors":"A. Wahid, A. Rao, Koushik Deb","doi":"10.1109/SKIMA.2018.8631526","DOIUrl":null,"url":null,"abstract":"An outlier is a point that could impact the data quality and the analysis result of data mining. The outlier detection may also be viewed as the pre-processing step for finding the objects that do not ensue the well-defined notions of predicted behavior in a data set. Recently, it emerges as a challenging issue in the real-world scenario to discover rare or novel events. This paper presents a novel and effective outlier detection method with kernel density estimation (KDE). A relative kernel-density based outlier factor (KDOF) is introduced to measure the outlier-ness score of an object in a given data sets. The proposed method is categorized into three phases. The first phase is to compute the local density at the given point using the KDE procedure. Then density fluctuation (DF) of each data point is calculated in step with the sum of the squares of the density differences between the point and its neighbors. Finally, the KDOF for each point is computed by comparing the density fluctuation at a given point with the average density fluctuation of its neighbors. Our experiments carried out on five real data sets have proven that the proposed method can perform better over other state-of-the-art outlier detection methods.","PeriodicalId":199576,"journal":{"name":"2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SKIMA.2018.8631526","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

An outlier is a point that could impact the data quality and the analysis result of data mining. The outlier detection may also be viewed as the pre-processing step for finding the objects that do not ensue the well-defined notions of predicted behavior in a data set. Recently, it emerges as a challenging issue in the real-world scenario to discover rare or novel events. This paper presents a novel and effective outlier detection method with kernel density estimation (KDE). A relative kernel-density based outlier factor (KDOF) is introduced to measure the outlier-ness score of an object in a given data sets. The proposed method is categorized into three phases. The first phase is to compute the local density at the given point using the KDE procedure. Then density fluctuation (DF) of each data point is calculated in step with the sum of the squares of the density differences between the point and its neighbors. Finally, the KDOF for each point is computed by comparing the density fluctuation at a given point with the average density fluctuation of its neighbors. Our experiments carried out on five real data sets have proven that the proposed method can perform better over other state-of-the-art outlier detection methods.
一种基于相对核密度的离群点检测算法
异常点是影响数据质量和数据挖掘分析结果的点。离群值检测也可以看作是在数据集中寻找不遵循预测行为的定义良好的概念的对象的预处理步骤。最近,在现实世界中发现罕见或新奇的事件成为一个具有挑战性的问题。提出了一种新颖有效的核密度估计离群点检测方法。引入基于相对核密度的离群因子(KDOF)来度量给定数据集中目标的离群度得分。该方法分为三个阶段。第一阶段是使用KDE过程计算给定点的局部密度。然后逐级计算每个数据点的密度涨落(DF)与该点与其相邻点的密度差平方和。最后,通过比较给定点的密度波动与其相邻点的平均密度波动来计算每个点的KDOF。我们在五个真实数据集上进行的实验证明,所提出的方法可以比其他最先进的离群值检测方法表现得更好。
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
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:604180095
Book学术官方微信