Angle-based outlier detection in high-dimensional data
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Statistical Analysis and Data Mining
Data Mining and Knowledge Discovery
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A bottleneck to detecting distance and density based outliers is that a nearest-neighbor search is required for each of the data points, resulting in a quadratic number of pairwise distance evaluations. In this paper, we propose a new method that uses the relative degree of density with respect to a fixed set of reference points to approximate the degree of density defined in terms of nearest neighbors of a data point. The running time of our algorithm based on this approximation is O(R_n log n) where n is the size of dataset and R is the number of reference points. Candidate outliers are ranked based on the outlier score assigned to each data point. Theoretical analysis and empirical studies show that our method is effective, efficient, and highly scalable to very large datasets.