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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) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
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
When Is ''Nearest Neighbor'' Meaningful?
Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_15 · Cited by 2,403
Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2733381 · Cited by 915
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
Generalized Out-of-Distribution Detection: A Survey
Yang, J., Zhou, K., Li, Y., & Liu, Z. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02117-4 · Cited by 884
Zhou, L., Burgoon, J. K., Nunamaker, J. F., & Twitchell, D. (2004) · Group Decision and Negotiation · doi:10.1023/b:grup.0000011944.62889.6f · Cited by 440
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
FP-outlier: Frequent pattern based outlier detection
He, Z., Xu, X., Huang, Z., & Deng, S. (2005) · Computer Science and Information Systems · doi:10.2298/csis0501103h · Cited by 188
A comprehensive survey of numeric and symbolic outlier mining techniques
Agyemang, M., Barker, K., & Alhajj, R. (2006) · Intelligent Data Analysis · doi:10.3233/ida-2006-10604 · Cited by 164
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
Statistical selection of relevant subspace projections for outlier ranking
Muller, E., Schiffer, M., & Seidl, T. (2011) · 2011 IEEE 27th International Conference on Data Engineering · doi:10.1109/icde.2011.5767916 · Cited by 126
Machine learning based approach to exam cheating detection
Kamalov, F., Sulieman, H., & Santandreu Calonge, D. (2021) · PLOS ONE · doi:10.1371/journal.pone.0254340 · Cited by 102
Event-based classification of social media streams
Reuter, T., & Cimiano, P. (2012) · Proceedings of the 2nd ACM International Conference on Multimedia Retrieval · doi:10.1145/2324796.2324824 · Cited by 98
Detecting outlying properties of exceptional objects
Angiulli, F., Fassetti, F., & Palopoli, L. (2009) · ACM Transactions on Database Systems · doi:10.1145/1508857.1508864 · Cited by 88
Discovering outlying aspects in large datasets
Vinh, N. X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2016) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0453-2 · Cited by 83
Methodologies in power systems fault detection and diagnosis
Aleem, S. A., Shahid, N., & Naqvi, I. H. (2015) · Energy Systems · doi:10.1007/s12667-014-0129-1 · Cited by 83
Detecting potential labeling errors in microarrays by data perturbation
Malossini, A., Blanzieri, E., & Ng, R. T. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl346 · Cited by 80
Mining class outliers: concepts, algorithms and applications in CRM
He, Z., Xu, X., Huang, J. Z., & Deng, S. (2004) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.07.002 · Cited by 79
Mining outlying aspects on numeric data
Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., & Tang, C. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0398-2 · Cited by 70
A decomposition of the outlier detection problem into a set of supervised learning problems
Paulheim, H., & Meusel, R. (2015) · Machine Learning · doi:10.1007/s10994-015-5507-y · Cited by 62
A new data mining based network Intrusion Detection model
Gudadhe, M., Prasad, P., & Kapil Wankhade, L. (2010) · 2010 International Conference on Computer and Communication Technology (ICCCT) · doi:10.1109/iccct.2010.5640375 · Cited by 59
Pang, G., Cao, L., Chen, L., & Liu, H. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0052 · Cited by 43
Mapping and classifying molecules from a high-throughput structural database
De, S., Musil, F., Ingram, T., Baldauf, C., & Ceriotti, M. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0192-4 · Cited by 42
Outlying property detection with numerical attributes
Angiulli, F., Fassetti, F., Manco, G., & Palopoli, L. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0458-x · Cited by 33
Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines
Tu, D., He, Y., Cui, W., Ge, S., Zhang, H., Han, S., Zhang, D., & Chaudhuri, S. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599776 · Cited by 14
OutRules: A Framework for Outlier Descriptions in Multiple Context Spaces
Müller, E., Keller, F., Blanc, S., & Böhm, K. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33486-3_57 · Cited by 7
Saybani, M. R. (2011) · Scientific Research and Essays · doi:10.5897/sre11.333 · Cited by 7
Metadata
- Authors
- Aggarwal, C. C., & Yu, P. S.
- Venue
- Proceedings of the 2001 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 2001-05
- Pages
- 37-46
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2061122559
- Cited by
- 1,029
- References
- 5
- FWCI
- 13.18
- Type
- conference paper
- Language
- en
- Open access
- closed
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