Algorithms for clustering data
Algorithms for clustering data
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Representation and Recognition of Handwritten Digits Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles
Machine Learning - Special issue: Unsupervised learning
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A Hidden Markov Model-Based Approach to Sequential Data Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A bayesian approach to temporal data clustering using the hidden markov model methodology
A bayesian approach to temporal data clustering using the hidden markov model methodology
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Multi-level background initialization using Hidden Markov Models
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Unsupervised scene analysis: a hidden Markov model approach
Computer Vision and Image Understanding
A clustering procedure for exploratory mining of vector time series
Pattern Recognition
Sensing Attacks in Computers Networks with Hidden Markov Models
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Taxonomy-driven lumping for sequence mining
Data Mining and Knowledge Discovery
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 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
Development of a wireless sensor glove for surgicalskills assessment
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
A novel clustering method on time series data
Expert Systems with Applications: An International Journal
Studying self- and active-training methods for multi-feature set emotion recognition
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Multiple classifier combination using reject options and markov fusion networks
Proceedings of the 14th ACM international conference on Multimodal interaction
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Hidden Markov models constitute a widely employed tool for sequential data modelling; nevertheless, their use in the clustering context has been poorly investigated. In this paper a novel scheme for HMM-based sequential data clustering is proposed, inspired on the similarity-based paradigm recently introduced in the supervised learning context. With this approach, a new representation space is built, in which each object is described by the vector of its similarities with respect to a predeterminate set of other objects. These similarities are determined using hidden Markov models. Clustering is then performed in such a space. By way of this, the difficult problem of clustering of sequences is thus transposed to a more manageable format, the clustering of points (vectors of features). Experimental evaluation on synthetic and real data shows that the proposed approach largely outperforms standard HMM clustering schemes.