Time series clustering for anomaly detection using competitive neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5629 LNCS, Issue , 2009, Pages 28-36

  Barreto, Guilherme A ^a   Aguayo, Leonardo ^b  

^a FEDERAL UNIVERSITY OF CEARÁ   (Brazil)

^b Nokia Technology Institute (INdT)   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; COMPETITIVE LEARNING; COMPETITIVE NEURAL NETWORK; DATA SAMPLE; DECISION THRESHOLD; FUZZY ART; PERFORMANCE COMPARISON; QUANTIZATION ERRORS; REAL-WORLD; TIME SERIES CLUSTERING; TIME-SERIES DATA; TRAINING DATA;

CONFORMAL MAPPING; FUZZY NEURAL NETWORKS; SELF ORGANIZING MAPS; TIME SERIES;

LEARNING ALGORITHMS;

EID: 69049086736     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02397-2_4     Document Type: Conference Paper

References (13)

1. Sarasamma, S.T., Zhu, Q.A.: Min-max hyperellipsoidal clustering for anomaly detection in network security. IEEE Transactions on Systems, Man and Cybernetics B-36(4), 887-901 (2006)
2. Barreto, G.A., Mota, J.C.M., Souza, L.G.M., Frota, R.A., Aguayo, L.: Condition monitoring of 3G cellular networks through competitive neural models. IEEE Transactions on Neural Networks 16(5), 1064-1075 (2005)
3. Lee, H.J., Cho, S.: SOM-based novelty detection using novel data. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 359-366. Springer, Heidelberg (2005)
4. Singh, S., Markou, M.: An approach to novelty detection applied to the classification of image regions. IEEE Transactions on Knowledge and Data Engineering 16(4), 1041-1047 (2004)
5. Zorriassatine, F., Al-Habaibeh, A., Parkin, R., Jackson, M., Coy, J.: Novelty detection for practical pattern recognition in condition monitoring of multivariate processes: a case study. International Journal of Advanced Manufacturing Technology 25(9-10), 954-963 (2005)
6. Jamsa-Jounela, S.L., Vermasvuori, M., Enden, P., Haavisto, S.: A process monitoring system based on the kohonen self-organizing maps. Control Engineering Practice 11(1), 83-92 (2003)
7. Hammer, B., Micheli, A., Sperduti, A., Strickert, M.: Recursive self-organizing network models. Neural Networks 17, 1061-1086 (2004)
8. Wong, M., Jack, L., Nandi, A.: Modified self-organising map for automated novelty detection applied to vibration signal monitoring. Mechanical Systems and Signal Processing 20(3), 593-610 (2006)
9. Kangas, J.A., Kohonen, T.K., Laaksonen, J.: Variants of self-organizing maps. IEEE Transactions on Neural Networks 1(1), 93-99 (1990)
10. Chappell, G.J., Taylor, J.G.: The temporal Kohonen map. Neural Networks 6(3), 441-445 (1993)
11. Koskela, T., Varsta, M., Heikkonen, J., Kaski, K.: Time series prediction using recurrent SOM with local linear models. International Journal of Knowledge-based Intelligent Engineering Systems 2(1), 60-68 (1998)
12. Carpenter, G.A., Grossberg, S., Rosen, D.B.: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4(6), 759-771 (1991)
13. Ferrell, B., Santuro, S.: NASA shuttle valve data (2005), http://www.cs.fit.edu/̃pkc/nasa/data/