AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm

TL;DR

AIDA introduces a parameter-free, distance-based anomaly detection algorithm with an analytical outlier score, along with TIX for feature-based explanations, excelling in high-dimensional data and subspace outlier detection.

Contribution

The paper presents AIDA, the first distance-based anomaly detection method without nearest-neighbour reliance, and introduces TIX for feature relevance explanations in high-dimensional datasets.

Findings

AIDA is competitive with state-of-the-art methods.

AIDA outperforms others in detecting subspace outliers.

TIX effectively identifies relevant outlier features in high-dimensional data.

Abstract

We combine the metrics of distance and isolation to develop the Analytic Isolation and Distance-based Anomaly (AIDA) detection algorithm. AIDA is the first distance-based method that does not rely on the concept of nearest-neighbours, making it a parameter-free model. Differently from the prevailing literature, in which the isolation metric is always computed via simulations, we show that AIDA admits an analytical expression for the outlier score, providing new insights into the isolation metric. Additionally, we present an anomaly explanation method based on AIDA, the Tempered Isolation-based eXplanation (TIX) algorithm, which finds the most relevant outlier features even in data sets with hundreds of dimensions. We test both algorithms on synthetic and empirical data: we show that AIDA is competitive when compared to other state-of-the-art methods, and it is superior in finding outliers hidden in multidimensional feature subspaces. Finally, we illustrate how the TIX algorithm is able to find outliers in multidimensional feature subspaces, and use these explanations to analyze common benchmarks used in anomaly detection.