Maximum likelihood estimation for multivariate mixture observations of Markov chins
IEEE Transactions on Information Theory
HMM Based On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Dynamic models for nonstationary signal segmentation
Computers and Biomedical Research
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Multi-level background initialization using Hidden Markov Models
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Integration of profile hidden Markov model output into association rule mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Hidden Markov Model approach for appearance-based 3D object recognition
Pattern Recognition Letters
Unsupervised scene analysis: a hidden Markov model approach
Computer Vision and Image Understanding
Spectral clustering with eigenvector selection
Pattern Recognition
Incremental and adaptive abnormal behaviour detection
Computer Vision and Image Understanding
Dynamic face recognition: From human to machine vision
Image and Vision Computing
Clustering-Based Construction of Hidden Markov Models for Generative Kernels
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A new distance measure for hidden Markov models
Expert Systems with Applications: An International Journal
A novel HMM-based clustering algorithm for the analysis of gene expression time-course data
Computational Statistics & Data Analysis
Unsupervised scene analysis: A hidden Markov model approach
Computer Vision and Image Understanding
Similarity-based classification of sequences using hidden Markov models
Pattern Recognition
Similarity-based clustering of sequences using hidden Markov models
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Domain ontology learning and consistency checking based on TSC approach and racer
RR'07 Proceedings of the 1st international conference on Web reasoning and rule systems
Recognition of human faces: from biological to artificial vision
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
A method for determination on HMM distance threshold
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
A review on time series data mining
Engineering Applications of Artificial Intelligence
State-space dynamics distance for clustering sequential data
Pattern Recognition
Section-wise similarities for clustering and outlier detection of subjective sequential data
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Spectral clustering for time series
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
ACM Computing Surveys (CSUR)
A new text clustering method using hidden Markov model
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
HMM-based hybrid meta-clustering ensemble for temporal data
Knowledge-Based Systems
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Clustering of sequential or temporal data is more challenging than traditional clustering as dynamic observations should be processed rather than static measures. This paper proposes a HiddenMarkov Model (HMM)-based technique suitable for clustering of data sequences. The main aspect of the work is the use of a probabilistic model-based approach using HMM to derive new proximity distances, in the likelihood sense, between sequences. Moreover, a novel partitional clustering algorithm is designed which alleviates computational burden characterizing traditional hierarchical agglomerative approaches. Experimental results show that this approach provides an accurate clustering partition and the devised distance measures achieve good performance rates. The method is demonstrated on real world data sequences, i.e. the EEG signals due to their temporal complexity and the growing interest in the emerging field of Brain Computer Interfaces.