Study and analysis of temporal data using Hidden Markov models

ICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services

Volumn , Issue , 2011, Pages 16-20

  Ghidouche, Kahina ^a   Kechadi, Tahar ^b   Tari, A Kamel ^a  

^a UNIVERSITY OF BEJAIA   (Algeria)

^b UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

clustering; hidden Markov model

Indexed keywords

CLUSTERING; NUMBER OF CLUSTERS; OBJECTIVE FUNCTIONS; OPTIMAL NUMBER; OPTIMAL STRUCTURES; TEMPORAL DATA;

DATA MINING; GEOGRAPHIC INFORMATION SYSTEMS; STRUCTURAL OPTIMIZATION;

HIDDEN MARKOV MODELS;

EID: 80052106541     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSDM.2011.5968122     Document Type: Conference Paper

References (18)

1. Baum-Walch., J. A.: An inequality with application to statistical estimation for probabilistic function of Markov processes to a model for ecology. Bull American Mathematical Society, 73:360-363, (1967).
2. Dermatas, E., Kokkinakis, G., Algorithm for clustering continuous density hmmby recognition error," IEEE Transactions on Speech and Audio Processing 4, pp. 231-234, (May 1996).
3. Juang, B-H.,Rabiner, L. R.: The segmental k-means algorithm for estimatingparameters of hidden Markov Models. IEEE transactions on acoustics, speech and signal processing,38(9): 1936-1941, (1990).
4. Kosaka, T., Masunaga, S., Kuraoka, M.: Speaker-independent phone modelling-based on speaker-dependent hmm's composition and clustering. Proceedings of the Twentieth International Conference on Acoustics, Speech, and Signal Processing, pp. 441-444, (1995).
5. Sebastiani, P., Ramoni, M., Cohen, P., Warwick, J., Davis, J. Discovering dynamics using Bayesian clustering. In D. J. Hand, J. N. Kok and M. R. Berthold (Eds.), Advances in intelligent data analysis, pp. 199-210. Berlin, Springer-Verlag. (1999).
6. Smyth, P.: Clustering sequences with hidden Markov models. In M. C. Mozer, M. I.Jordan and T. Petsche (Eds.), Advances in neural information processing, pp. 648-654. Cambridge:MA, MIT Press.(1997).
7. Viterbi, A. J. Error bounds for convolutional codes and asymptotically optimum decoding algorithm. IEEE transactions on information theory, pp. 260-269 (1967).
8. Rabiner, L. R., Lee C. H., Juang B. H., Wilpon, J. G.: Hmm clustering for connected word recognition," in Proceedings of the International Conference onAcoustics, Speech, and Signal Processing, (1989).
9. Cen. L,Gautam, B.: Unsupervised classification on temporal data." Survey paper, Department of Computer Science, Vanderbilt University, Apr. 1998.
10. CHRU, centre hospitalier regional universitaire de Lille de France.
11. A. Chaturvedi, P. Green, and J. Carroll. K-means, k-medians and k-modes: Special cases of partitioning multi-way data. In The Classification Society of North America (CSNA) Meeting Presentation, Houston, TX, 1994.
12. Jonathan Alon, Vladimir Pavlovic, Discovering Clusters in Motion Time-Series Data, IEEE CVPR, June 2003.
13. Tim Oates, Laura Firoin, Paul R.Cohen: Clustering Time Series with Hidden Markov Models and Dynamic Time Warping,2001.
14. Bernhard Knab1, Alexander Schliep2, Barthel Steckemetz3, and Bernd, Wichern:, Model- based clustering with Hidden Markov Models and its application to financial time-series data, Springer, 561-569, 2003.
15. JouniHelske, MerviEerola, IoanTabus: MINIMUM DESCRIPTION LENGTH BASED HIDDEN MARKOV MODEL CLUSTERING FOR LIFE SEQUENCE ANALYSIS, 2010.
16. K,dbnv;nj, L. E. Baum, T. Petrie, G. Soules, and N. Weiss, \A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains," The Annuals of Mathematical Statistics 4(1), pp. 164(171, 1970.
17. A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm," Journal of Royal Statistical Society Series B (methodological) 39, pp. 1-38, 1977.
18. Z. Ghahramani and M. I. Jordan, Factorial hidden Markov models," Tech. Rep. 9502, MIT Computer Cognitive Science, Aug. 1995.