Classification of Outlier’s Detection Methods Based on Quantitative or Semantic Learning-Reference-Cited by-同舟云学术

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Classification of Outlier’s Detection Methods Based on Quantitative or Semantic Learning

Published:2020 Issue: Volume: Page:45-59
ISSN:1613-5113
Container-title:Advanced Sciences and Technologies for Security Applications
language:
Short-container-title:

Author:

Kashef Rasha,Gencarelli Michael,Ibrahim Ahmed

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-35642-2_3

Reference19 articles.

1. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proc. of the 11th Int. Conf. on data engineering, pp 3–14. https://doi.org/10.1016/j.jbi.2007.05.004.

2. Aleskerov E, Freisleben B, Rao B (1997) Cardwatch : a neural network based database stern for credit card fraud detection. Proc IEEE/IAFE:220–226. https://doi.org/10.1109/CIFER.1997.618940

3. Bhaduri K, Matthews BL, Giannella CR (2011) Algorithms for speeding up distance-based outlier detection. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 859–67. https://doi.org/10.1145/2020408.2020554

4. Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM Sigmod international conference on management of data, pp 1–12. https://doi.org/10.1145/335191.335388

5. Cooper GF, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309–347. https://doi.org/10.1023/A:1022649401552

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