Learning states and rules for detecting anomalies in time series

Applied Intelligence

Volumn 23, Issue 3, 2005, Pages 241-255

  Salvador, Stan ^a   Chan, Philip ^a  

^a Florida Institute of Technology   (United States)

Author keywords

Anomaly detection; Cluster validation; Clustering; Segmentation; Time series

Indexed keywords

ANOMALY DETECTION; CLUSTER VALIDATION; CLUSTERING; SEGMENTATION;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); EXPERT SYSTEMS; FINITE AUTOMATA; LEARNING SYSTEMS;

TIME SERIES ANALYSIS;

EID: 29144509475     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-005-4610-3     Document Type: Article

References (28)

1. W. Cohen, "Fast effective rule induction," in Proc. of the 12 Intl. Conference on Machine Learning, Tahoe City, CA, 1995, pp. 115-123.
2. S. Salvador and P. Chan, "Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms," Laboratory for Learning Research, Florida Institute of Technology, Melbourne, FL, Technical Report TR-2003-18, 2003.
3. R. Ng and J. Hah, "Efficient and effective clustering methods for spatial data mining," in The 20th Intl. Conf. On Very Large Data Bases, Santiago, Chile, 1994, pp. 12-15.
4. M. Ester, H. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Proc. 3rd Intl. Conf. on Knowledge Discovery and Data Mining, Portland OR, 1996, pp. 226-231.
5. A. Hinneburg and D. Keim, "An efficient approach to clustering in large multimedia databases with noise," in Proc 4th Intl. Conf. on Knowledge Discovery and Data Mining. New York City, NY, 1998, pp. 58-65.
6. S. Guha, R. Rastogi, and K. Shim, "ROCK: A robust clustering algorithm for categorical attributes," in The 15th Intl. Conf. on Data Engineering, Sydney, Australia, 1999, pp. 512-523.
7. G. Karypis, E. Han, and V. Kumar, "Chameleon: A hierarchical clustering algorithm using dynamic modeling," IEEE Computer, vol. 32, no 8, pp. 68-75, 1999.
8. G. Seikholeslami, S. Chatterjee and A. Zhang,:WaveCluster: A multi-resolution clustering approach for very large spatial databases," in Proc. of the 24th VLDB, New York City, New York, 1998, pp. 428-439.
9. E. Keogh, S. Chu, D. Hart and M. Pazanni, "An online algorithm for segmenting time series," in Proc. IEEE Intl. Conf. on Data Mining, San Jose, CA, 2001, pp. 289-296.
10. P. Smyth, "Clustering using Monte-Carlo cross-validation," in Proc. 2nd KDD, Portland, OR, 1996, pp. 126-133.
11. R. Baxter and J. Oliver, "The kindest cut: minimum message length segmentation," in Algorithmic Learning Theory, 7th Intl. Workshop, Sydney, Australia, 1996, pp. 83-90.
12. M. Hansen and B. Yu, "Model selection and the principle of minimum description length," JASA, vol. 96, pp.746-774, 2001.
13. C. Fraley and E. Raftery, "How many clusters? Which clustering method? Answers via model-based Cluster Analysis," Computer Journal, vol. 41, pp. 578-588, 1998.
14. M. Sugiyama and H. Ogawa, Subspace Information criterion for model selection, Neural Computation, vol. 13, no.8, pp. 1863-1889, 2001.
15. K. Vasko and T. Toivonen. "Estimating the number of segments in time series data using permutation tests," in Proc. IEEE Intl. Conf. on Data Mining, Maebashi City, Japan, 2002, pp. 466-47.
16. V. Roth, T. Lange, M. Braun, and J. Buhmann, "A resampling approach to cluster validation," in Proc. in Computational Statistics: 15th Symposium (COMPSTAT2002), Berlin, Germany, 2002, pp. 123-128.
17. S. Monti, P. Tamayo, J. Mesirov, and T Golub, "Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data," Machine Learning, vol. 52, nos. 1-2, pp. 91-118, 2003.
18. R. Tibshirani, G. Walther, and T. Hastie, " Estimating the number of clusters in a dataset via the Gap statistic," Dept. of Biostatistics, Stanford Univ., Stanford, CA, Technical Report 208, 2001.
19. R. Tibshirani, G. Walther B. Botstein, and P. Brown, "Cluster validation by prediction strength," Dept. of Biostatistics, Stanford Univ., Stanford, CA, Technical Report 2001-21, 2001.
20. T. Chiu, D. Fang, J. Chen, Y. Wang, and C. Jeris, "A robust and scalable clustering algorithm for mixed type attributes in large database environment," in Proc. of the 7th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, 2001, pp. 263-268.
21. A. Foss and A. Zaiane, "A parameterless method for efficiently discovering clusters of arbitrary shape in large datasets." in Proc. of the 2002 IEEE Intl. Conf. on Data Mining (ICDM'02), Maebashi City, Japan, 2002, pp. 179-186.
22. S. Harris, D. Hess, and J. Venegas, "An objective analysis of the pressure-volume curve in the acute respiratory distress syndrome," American Journal of Respiratory and Critical Care Medicine, vol. 161, no. 2, pp. 432-439, 2000.
23. D. Dasgupta and S. Forrest, "novelty detection in time series data using ideas from immunology," In Proc. Fifth Intl. Conf. on Intelligent Systems, Reno, NV, 1996, pp. 82-87.
24. T. Caudell and D. Newman, "An adaptive resonance architecture to define normality and detect novelties in time series and databases," in Proc. IEEE World Congress on Neural Networks, Portland, OR, pp. IV166-176. 1993.
25. P. Langley, D. George, S. Bay, and K. Saito, "Robust induction of process models from time-series data," in Proc. of the 20th Intl. Conf. on Machine Learning, Washington, DC, 2003, pp. 32-439.
26. E. Weisstein, "Least squares fitting," From MathWorld-A Wolfram Web Resource, [http://mathworld.wolfram.com/ Least-SquaresFitting.html].
27. J. Furnkranz and G. Wildmer, "Incremental reduced error pruning," in Proc. Intl. Conf. on Machine Learning, New Brunswick, NJ, 1994, pp. 70-77.
28. E. Keogh and T. Folias, The UCR Time Series Data Mining Archive [http://www.cs.ucr.edu/~eamonn/TSDMA/index. html]. Riverside, CA. University of California-Computer Science and Engineering Department, 2004.