Fast Mining of Distance-Based Outliers in High-Dimensional Datasets

Abstract

Defining outliers by their distance to neighboring data points has been shown to be an effective non-parametric approach to outlier detection. Existing algorithms for mining distance-based outliers do not scale to large, highdimensional data sets. In this paper, we present RBRP, a fast algorithm for mining distance-based outliers, particularly targeted at high-dimensional data sets. RBRP scales log-linearly as a function of the number of data points and linearly as a function of the number of dimensions. Our empirical evaluation demonstrates that we outperform the state-of-the-art, often by an order of magnitude.