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Volumn 2, Issue , 2008, Pages 472-482

Mining abnormal patterns from heterogeneous time-series with irrelevant features for fault event detection

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; FAULT SLIPS; ITERATIVE METHODS; TIME SERIES;

EID: 52649167834     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972788.43     Document Type: Conference Paper
Times cited : (3)

References (27)
  • 9
    • 84898963788 scopus 로고    scopus 로고
    • Object classification from a single example utilizing class relevance metrics
    • M. Fink. Object classification from a single example utilizing class relevance metrics. In Advances in Neural Information Processing Systems 17, pages 449-456, 2005.
    • (2005) Advances in Neural Information Processing Systems , vol.17 , pp. 449-456
    • Fink, M.1
  • 11
    • 52649137809 scopus 로고    scopus 로고
    • V. Guralnik and J. Srivastava. Event detection from time series data. In Proceedings of the 5th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 33-42, 1999.
    • V. Guralnik and J. Srivastava. Event detection from time series data. In Proceedings of the 5th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 33-42, 1999.
  • 19
    • 33644653840 scopus 로고    scopus 로고
    • A unifying framework for detecting outliers and change points from time series
    • J. Takeuchi and K. Yamanishi. A unifying framework for detecting outliers and change points from time series. IEEE Transactions on Knowledge and Data Engineering, 18(4):482-492, 2006.
    • (2006) IEEE Transactions on Knowledge and Data Engineering , vol.18 , Issue.4 , pp. 482-492
    • Takeuchi, J.1    Yamanishi, K.2
  • 24
    • 34547991011 scopus 로고    scopus 로고
    • X. Xuan and K. Murphy. Modeling changing dependency structure in multivariate time series. In Proceedings of the 24th international conference on Machine learning, pages 1055-1062, 2007.
    • X. Xuan and K. Murphy. Modeling changing dependency structure in multivariate time series. In Proceedings of the 24th international conference on Machine learning, pages 1055-1062, 2007.
  • 25
    • 3543125360 scopus 로고    scopus 로고
    • On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
    • K. Yamanishi, J. Takeuchi, G. Williams, and P. Milne. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms, Data Mining and Knowledge Discovery Journal, 8(3):275-300, 2004.
    • (2004) Data Mining and Knowledge Discovery Journal , vol.8 , Issue.3 , pp. 275-300
    • Yamanishi, K.1    Takeuchi, J.2    Williams, G.3    Milne, P.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.