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Volumn 19, Issue , 2013, Pages 1174-1181

A hierarchical framework using approximated local outlier factor for efficient anomaly detection

Author keywords

Anomaly detection; Hamming distance; Local outlier factor; Local sensitive hashing

Indexed keywords


EID: 84896971289     PISSN: 18770509     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2013.06.168     Document Type: Conference Paper
Times cited : (30)

References (27)
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    • Zhang, Y.1    Meratnia, N.2    Havinga, P.3
  • 8
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    • (2012) rd Rapid Miner Community Meeting and Conference (RCOMM 2012) , pp. 1-12
    • Aner, N.1    Goldstein, M.2
  • 14
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    • Outliers in statistical data
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    • (1980) Technometrics , vol.22 , Issue.4 , pp. 631-631
    • Young, W.R.1
  • 15
    • 84948141376 scopus 로고    scopus 로고
    • A mixture approach to novelty detection using training data with outliers
    • M. Lauer, A mixture approach to novelty detection using training data with outliers, in: Proceedings of the 12th European Conference on Machine Learning, EMCL '01, 2001, pp. 300-311.
    • (2001) thEuropean Conference on Machine Learning, EMCL '01 , pp. 300-311
    • Lauer, M.1
  • 26
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    • D. N. A. Asuncion
    • D. N. A. Asuncion, UCI machine learning repository, http://www.ics.uci. edu/mlearn/MLRepository.html (2007).
    • (2007) UCI Machine Learning Repository


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.