Triangle-based outlier detection

doi:10.1016/j.patrec.2022.03.008

Triangle-based outlier detection

2022-04-01

6

PRE

AI

J

Jorge Navarro *

I

Isaac Martín de Diego

DOI：10.1016/j.patrec.2022.03.008

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Abstract

En 中文

For the last decades, anomaly detection has been one of the most common problems in data mining and computer science projects. The scientific community has made a great effort to develop methods and techniques for the detection of elements that deviate from the norm. Many of these techniques follow an unsupervised approach since anomalies are scarce and not easily reproducible. In this paper, a novel unsupervised anomaly detection method based on geometric reasoning is proposed. Triangle-based Outlier Detection (TOD) falls within the group of distance-based anomaly detection techniques and has been compared to state of the art methods for unsupervised anomaly detection. Results achieved prove that TOD offers greater robustness than alternative methods for the selection of the decision thresholds that condition the detection of outliers.(c) 2022 Elsevier B.V. All rights reserved.

Keywords:

Unsupervised learning

Anomaly detection

Information fusion

Dissimilarity matrices

Triangle inequality

Triangle-based outlier detection

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