The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Outlier detection for high dimensional data
Abstract
The outlier detection problem has important applications in the field of fraud detection, network robustness analysis, and intrusion detection. Most such applications are high dimensional domains in which the data can contain hundreds of dimensions. Many recent algorithms use concepts of proximity in order to find outliers based on their relationship to the rest of the data. However, in high dimensional space, the data is sparse and the notion of proximity fails to retain its meaningfulness. In fact, the sparsity of high dimensional data implies that every point is an almost equally good outlier from the perspective of proximity-based definitions. Consequently, for high dimensional data, the notion of finding meaningful outliers becomes substantially more complex and non-obvious. In this paper, we discuss new techniques for outlier detection which find the outliers by studying the behavior of projections from the data set.
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Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149
Hawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982
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A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596
A survey on unsupervised outlier detection in high‐dimensional numerical data
Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864
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
Outlier mining in large high-dimensional data sets
Angiulli, F., & Pizzuti, C. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.31 · Cited by 365
CatchSync: catching synchronized behavior in large directed graphs
Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623632 · Cited by 151
Metadata
- Authors
- Aggarwal, C. C., & Yu, P. S.
- Venue
- ACM SIGMOD Record
- Published
- 2001-05
- Volume
- 30
- Issue
- 2
- Pages
- 37-46
- ISSN
- 0163-5808 (ISSN-L)
- OpenAlex
- W4254420769
- Cited by
- 442
- References
- 3
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- closed
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