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Volumn 1, Issue , 2004, Pages 306-311

Learning states and rules for time series anomaly detection

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; HUMAN EXPERTS; LEARNING STATES; LOGICAL RULES;

EID: 10044227402     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (46)

References (15)
  • 1
    • 3543075602 scopus 로고
    • An adaptive resonance architecture to define normality and detect novelties in time series and databases
    • Portland, OR
    • Caudell, T. and Newman, D. 1993. An Adaptive Resonance Architecture to Define Normality and Detect Novelties in Time Series and Databases. In Proc. IEEE World Congress on Neural Networks, 166-176. Portland, OR.
    • (1993) Proc. IEEE World Congress on Neural Networks , pp. 166-176
    • Caudell, T.1    Newman, D.2
  • 6
    • 0032686723 scopus 로고    scopus 로고
    • Chameleon: A hierarchical clustering algorithm using dynamic modeling
    • Karypis, G.; Han, E.; and Kumar, V. 1999. Chameleon: A hierarchical clustering algorithm using dynamic modeling. IEEE Computer, 32(8):68-75.
    • (1999) IEEE Computer , vol.32 , Issue.8 , pp. 68-75
    • Karypis, G.1    Han, E.2    Kumar, V.3
  • 8
    • 0038724494 scopus 로고    scopus 로고
    • Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
    • Monti, S. et al. 2003. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning, 52(1-2):91-118.
    • (2003) Machine Learning , vol.52 , Issue.1-2 , pp. 91-118
    • Monti, S.1
  • 9
    • 10044256143 scopus 로고    scopus 로고
    • Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms
    • CS-2003-18, Dept. of Computer Sciences, Florida Institute of Technology
    • Salvador, S. and Chan, P. 2003. Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms, Technical Report, CS-2003-18, Dept. of Computer Sciences, Florida Institute of Technology.
    • (2003) Technical Report
    • Salvador, S.1    Chan, P.2
  • 10
    • 10044239002 scopus 로고    scopus 로고
    • Learning states and rules for time series anomaly detection
    • CS-2003-05, Dept. of Computer Sciences, Florida Institute of Technology
    • Salvador, S.; Chan, P.; and Brodie, J. 2003 Learning States and Rules for Time Series Anomaly Detection, Technical Report, CS-2003-05, Dept. of Computer Sciences, Florida Institute of Technology.
    • (2003) Technical Report
    • Salvador, S.1    Chan, P.2    Brodie, J.3
  • 13
    • 0012452913 scopus 로고    scopus 로고
    • Cluster validation by prediction strength
    • 2001-21, Dept. of Biostatistics, Stanford Univ
    • Tibshirani, R. et al. 2001. Cluster Validation by Prediction Strength, Technical Report, 2001-21, Dept. of Biostatistics, Stanford Univ.
    • (2001) Technical Report
    • Tibshirani, R.1
  • 14
    • 0003414440 scopus 로고    scopus 로고
    • Estimating the number of clusters in a dataset via the Gap statistic
    • Dept. of Biostatistics, Stanford Univ
    • Tibshirani, R.; Walther, G.; and Hastie, T. 2000. Estimating the number of clusters in a dataset via the Gap statistic. Technical Report, 208, Dept. of Biostatistics, Stanford Univ.
    • (2000) Technical Report , vol.208
    • Tibshirani, R.1    Walther, G.2    Hastie, T.3
  • 15
    • 1642433203 scopus 로고    scopus 로고
    • Estimating the number of segments in time series data using permutation tests
    • Maebashi City, Japan
    • Vasko, K. and T. Toivonen. 2002. Estimating the number of segments in time series data using permutation tests. In Proc. IEEE Intl. Conf. on Data Mining, 466-473, Maebashi City, Japan.
    • (2002) Proc. IEEE Intl. Conf. on Data Mining , pp. 466-473
    • Vasko, K.1    Toivonen, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.