VOA*: Fast Angle-Based Outlier Detection over High-Dimensional Data Streams

Abstract

Outlier detection in the high-dimensional data stream is a challenging data mining task. In high-dimensional data, the distance-based measures of outlierness become less effective and unreliable. Angle-based outlier detection ABOD technique was proposed as a more suitable scheme for high-dimensional data. However, ABOD is designed for static datasets and its naive application on a sliding window over data streams will result in poor performance. In this research, we propose two incremental algorithms for fast outlier detection based on an outlier threshold value in high-dimensional data streams: IncrementalVOA and \(VOA^{*}\). IncrementalVOA is a basic incremental algorithm for computing outlier factor of each data point in each window. \(VOA^{*}\) enhances the incremental computation by using a bound-based pruning method and a retrospect-based incremental computation technique. The effectiveness and efficiency of the proposed algorithms are experimentally evaluated on synthetic and real world datasets where \(VOA^{*}\) outperformed other methods.