Triangle-based outlier detection

Abstract

• Anomaly detection is one of the most common problems in data science. • Unsupervised approaches are useful since anomalies are not easily reproducible. • Triangle-based outlier detection is based on geometric reasoning . • Results achieved show a higher robustness than state of the art alternatives. 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.