Escape velocity-based adaptive outlier detection algorithm

Abstract

Outlier detection is a pivotal technique within the realm of data mining, serving to pinpoint aberrant values nestled within datasets. It has been widely employed across diverse domains, including detection of credit card frauds, identification of seismic activities, and identification of anomalies within image datasets. However, existing approaches still face three shortcomings: (1) they often struggle with the intricacies of parameter selection and the vexing top-n dilemma, (2) they lack in their capacity to discern local outliers, and (3) their algorithmic efficacies markedly wane as datasets burgeon in sample point size and outlier prevalence. In addressing these formidable hurdles, we propose a novel, Escape Velocity-based adaptive Outlier Detection algorithm, noted as EVOD. The EVOD algorithm calculates the escape velocity of each data sample point and automatically detects the number of outliers by monitoring peak fluctuations in the growth rate of escape velocities of sample points, thereby solving the top-n problem suffered by existing outlier detection algorithms. Experimental results demonstrate that our algorithm, without requiring manual adjustment of parameters, can simultaneously detect global outliers, local outliers, and outlier clusters. In addition, it maintains a good performance even as the number of sample points and outliers in the dataset increases, particularly for complex manifold datasets.