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Proceedings Paper

Clustering sequence data using hidden Markov model representation
Author(s): Cen Li; Gautam Biswas
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Paper Abstract

This paper proposes a clustering methodology, for sequence data, using hidden Markov model (HMM) representation. The proposed methodology 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 the clustering process, and (ii) it provides objective criterion function, to select the optimal clustering partition. The algorithm is presented in terms of four nested levels of searches: (i) the search for the optimal number of clusters in a partition, (ii) the search for the optimal structure for a given partition, (iii) the search for the optimal HMM structure for each cluster, and (iv) the search for the optimal HMM parameters for each HMM. Preliminary results are given to support the proposed methodology.

Paper Details

Date Published: 25 February 1999
PDF: 8 pages
Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339979
Show Author Affiliations
Cen Li, Vanderbilt Univ. (United States)
Gautam Biswas, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 3695:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology
Belur V. Dasarathy, Editor(s)

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