A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data

Outlier mining in d-dimensional point sets is a fundamental and well studied data mining task due to its variety of applications. Most such applications arise in high-dimensional domains. A bottleneck of existing approaches is that implicit or explicit assessments on concepts of distance or nearest neighbor are deteriorated in high-dimensional data. Following up on the work of Kriegel et al. (KDD ’08), we investigate the use of angle-based outlier factor in mining highdimensional outliers. While their algorithm runs in cubic time (with a quadratic time heuristic), we propose a novel random projection-based technique that is able to estimate the angle-based outlier factor for all data points in time nearlinear in the size of the data. Also, our approach is suitable to be performed in parallel environment to achieve a parallel speedup. We introduce a theoretical analysis of the quality of approximation to guarantee the reliability of our estimation algorithm. The empirical experiments...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Ninh Pham, Rasmus Pagh
• Keywords: Data Mining, Dimensional Data Sets, Estimation Algorithm, KDD 2012, Parallel Speedup