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Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Abstract
Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.
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Cited by
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.17 · Cited by 6,601
Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649
Progress in Outlier Detection Techniques: A Survey
Wang, H., Bah, M. J., & Hammad, M. (2019) · IEEE Access · doi:10.1109/access.2019.2932769 · Cited by 523
Metadata
- Authors
- Bay, S. D., & Schwabacher, M.
- Venue
- Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03
- Publisher
- ACM Press
- Published
- 2003
- OpenAlex
- W4238722012
- Cited by
- 189
- References
- 0
- FWCI
- 3.19
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bay, S. D., & Schwabacher, M. (2003). Mining distance-based outliers in near linear time with randomization and a simple pruning rule. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03. https://doi.org/10.1145/956755.956758
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