Minimum Description Length Based Hidden Markov Model Clustering for Life Sequence Analysis
Helske, J., Eerola, M., & Tabus, I. (2010). Minimum Description Length Based Hidden Markov Model Clustering for Life Sequence Analysis. In Workshop on Information Theoretic Methods in Science and Engineering. http://sp.cs.tut.fi/WITMSE10/Proceedings/index.html
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2010Copyright
© The Authors 2010
In this article, a model-based method for clustering life
sequences is suggested. In the social sciences, model-free
clustering methods are often used in order to find typical
life sequences. The suggested method, which is based on
hidden Markov models, provides principled probabilistic
ranking of candidate clusterings for choosing the best solution. After presenting the principle of the method and
algorithm, the method is tested with real life data, where
it finds eight descriptive clusters with clear probabilistic
structures.
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Workshop on Information Theoretic Methods in Science and Engineering
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http://sp.cs.tut.fi/WITMSE10/Proceedings/index.htmlPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/20099158
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