Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Discovering Dynamics Using Bayesian Clustering
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Mining Temporal Patterns from Health Care Data
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Enhanced human behavior recognition using HMM and evaluative rectification
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Rule-based trajectory segmentation for modeling hand motion trajectory
Pattern Recognition
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This paper describes a clustering methodology for temporal data using hidden Markov model(HMM) representation. The proposed method improves upon existing HMM based clustering methods in two ways: (i) it enables HMMs to dynamically change its model structure to obtain a better fit model for data during clustering process, and (ii) it provides objective criterion function to automatically select the clustering partition. The algorithm is presented in terms of four nested levels of searches: (i) the search for the number of clusters in a partition, (ii) the search for the structure for a fixed sized partition, (iii) the search for the HMM structure for each cluster, and (iv) the search for the parameter values for each HMM. Preliminary experiments with artificially generated data demonstrate the effectiveness of the proposed methodology.