Maximum likelihood estimation for multivariate mixture observations of Markov chins
IEEE Transactions on Information Theory
Self-Organizing Maps
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Efficient greedy learning of Gaussian mixture models
Neural Computation
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This paper provides a constructive algorithm in which a hierarchical tree of hidden Markov models may be obtained directly from data using an unsupervised learning regime. The method is applied to health insurance transaction data such that profiles with similar local temporal behaviours are grouped together. By judicious incorporation of limited additional prior information, it is found that profiles can be separated into various sub-behavioural groups thus providing a technique for large-scale automatic labelling of data. In the application to the health insurance transaction data set, by incorporating limited information concerning the medical functions used in a medical procedure, it is possible to label some individual medical transactions as to whether they are related to a particular medical condition or not. This automatic labelling process adds values to the collected transactional database for possible further applications, e.g. public health studies.