The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Fast Outlier Detection in High Dimensional Spaces
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Citations
Cites
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
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
Barnett, V., Lewis, T., & Abeles, F. (1979) · Physics Today · doi:10.1063/1.2995737 · Cited by 2,824
Efficient algorithms for mining outliers from large data sets
Ramaswamy, S., Rastogi, R., & Shim, K. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335437 · Cited by 1,860
Atkinson, A., Barnett, V., & Lewis, T. (1995) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2983451 · Cited by 1,682
Algorithms for Mining Distance-Based Outliers in Large Datasets
Knorr, E. M., & Ng, R. T. (1998) · Cited by 1,544
Sagan, H. (1994) · Universitext · doi:10.1007/978-1-4612-0871-6 · Cited by 1,336
Efficient algorithms for mining outliers from large data sets
Ramaswamy, S., Rastogi, R., & Shim, K. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335437 · Cited by 1,171
Outlier detection for high dimensional data
Aggarwal, C. C., & Yu, P. S. (2001) · Proceedings of the 2001 ACM SIGMOD international conference on Management of data · doi:10.1145/375663.375668 · Cited by 1,029
Research works printed but not linked: 5
- C. Faloutsos and S. Roseman. Fractals for secondary key retrieval. In Proc. ACM Int. Conf. on Principles of Database Systems (PODS’89), pages 247–252, 1989. · doi:10.1145/73721.73746
- H. V. Jagadish. Linear clustering of objects with multiple atributes. In Proc. ACM Int. Conf. on Managment of Data (SIGMOD’90), pages 332–342, 1990. · doi:10.1145/93597.98742
- H. V. Jagadish. Linear clustering of objects with multiple atributes. In Proc. ACM Int. Conf. on Managment of Data (SIGMOD’90), pages 332–342, 1990. · doi:10.1145/93597.98742
- M. Lopez and S. Liao. Finding k-closest-pairs efficiently for high dimensional data. In Proc. 12th Canadian Conf. on Computational Geometry (CCCG), pages 197–204, 2000.
- J. Shepherd, X. Zhu, and N. Megiddo. A fast indexing method for multidimensional nearest neighbor search. In Proc. SPIE Conf. on Storage and Retrieval for image and video databases VII, pages 350–355, 1999. · doi:10.1117/12.333854
Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
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
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Deng, A., & Hooi, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16523 · Cited by 1,413
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
Goldstein, M., & Uchida, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0152173 · Cited by 1,032
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
Metadata
- Authors
- Angiulli, F., & Pizzuti, C.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2002
- Pages
- 15-27
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W1530232915
- Cited by
- 836
- References
- 17
- FWCI
- 5.98
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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- The paper
- Angiulli, F., & Pizzuti, C. (2002). Fast Outlier Detection in High Dimensional Spaces. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-45681-3_2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
