article

Discovering cluster-based local outliers

Authors:

Shengchun DengAuthors Info & Claims

Pattern Recognition Letters, Volume 24, Issue 9-10

Pages 1641 - 1650

https://doi.org/10.1016/S0167-8655(03)00003-5

Published: 01 June 2003 Publication History

Abstract

In this paper, we present a new definition for outlier: cluster-based local outlier, which is meaningful and provides importance to the local data behavior. A measure for identifying the physical significance of an outlier is designed, which is called cluster-based local outlier factor (CBLOF). We also propose the FindCBLOF algorithm for discovering outliers. The experimental results show that our approach outperformed the existing methods on identifying meaningful and interesting outliers.

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Cited By

Zheng LChen LWang Y(2024)A new unsupervised outlier detection methodJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23651846:1(1713-1734)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-236518
Li ZWei SLiu S(2024)Outlier detection using conditional information entropy and rough set theoryJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23600946:1(1899-1918)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-236009
Wang XDuan LYu ZHe CBao Z(2024)Robust Multi-Kernel Nearest Neighborhood for Outlier DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336417936:8(4220-4231)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3364179
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Index Terms

Discovering cluster-based local outliers
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published: 01 June 2003

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Cited By

Zheng LChen LWang Y(2024)A new unsupervised outlier detection methodJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23651846:1(1713-1734)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-236518
Li ZWei SLiu S(2024)Outlier detection using conditional information entropy and rough set theoryJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23600946:1(1899-1918)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-236009
Wang XDuan LYu ZHe CBao Z(2024)Robust Multi-Kernel Nearest Neighborhood for Outlier DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336417936:8(4220-4231)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3364179
Wang XDuan LHe CChen YWu X(2024)An Efficient Adaptive Multi-Kernel Learning With Safe Screening Rule for Outlier DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333070836:8(3656-3669)Online publication date: 1-Aug-2024
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Kiersztyn APylak DHorodelski MKiersztyn KUrbanovich P(2024)Random clustering-based outlier detectorInformation Sciences: an International Journal10.1016/j.ins.2024.120498667:COnline publication date: 1-May-2024
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Chen BLi YPeng DChen HYuan Z(2024)Fusing multi-scale fuzzy information to detect outliersInformation Fusion10.1016/j.inffus.2023.102133103:COnline publication date: 1-Mar-2024
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Hu BWang XZhou PDu L(2024)Multi-view Outlier Detection via Graphs DenoisingInformation Fusion10.1016/j.inffus.2023.102012101:COnline publication date: 1-Jan-2024
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Wang SYuan ZLuo CChen HPeng D(2024)Exploiting fuzzy rough entropy to detect anomaliesInternational Journal of Approximate Reasoning10.1016/j.ijar.2023.109087165:COnline publication date: 1-Feb-2024
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Wu ZYang XWei XYuan PZhang YBai J(2024)A self-supervised anomaly detection algorithm with interpretabilityExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121539237:PBOnline publication date: 1-Feb-2024
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