LOF: identifying density-based local outliers
Open accessAbstract
For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical.
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- Authors
- Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- University of British Columbia (CA)
- Venue
- Proceedings of the 2000 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 2000-05-16
- Pages
- 93-104
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W4254182148
- Cited by
- 4,149
- References
- 0
- Citations by year
- 2026: 445 · 2025: 595 · 2024: 512 · 2023: 499 · 2022: 355 · 2021: 328 · 2020: 243 · 2019: 147 · 2018: 148 · 2017: 102 · 2016: 89 · 2015: 69 · 2014: 76 · 2013: 64 · 2012: 68
- FWCI
- 7.61
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGMOD International Conference on Management of Data (SIGMOD) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Keywords
- local outlier factor, outlier detection, local outliers, e-commerce fraud detection
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