Clustering Sequences with Hidden Markov Models

This paper discusses a probabilistic model-based approach to clustering sequences,using hidden Markov models (HMMs) . The problem can be framed as a generalization of the standard mixture model approach to clustering in feature space. Two primary issues are addressed. First, a novel parameter initialization procedure is proposed, and second, the more difficult problem of determining the number of clusters K, from the data, is investigated. Experimental resultsindicate that the proposed techniques are useful for revealing hidden cluster structure in data sets of sequences.