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Minimum description length based hidden Markov model clustering for life sequence analysis
Department of Signal Processing, Tampere University of Technology, Tampere, FINLAND.ORCID iD: 0000-0001-7130-793X
Methodology Centre for Human Sciences, University of Jyväskylä, FINLAND.
Department of Signal Processing, Tampere University of Technology, Tampere, FINLAND.
2010 (English)In: Proceedings of the Third Workshop on Information Theoretic Methods in Science and Engineering, August 16-18, 2010, Tampere, Finland, 2010Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2010.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-144916OAI: oai:DiVA.org:liu-144916DiVA, id: diva2:1180697
Conference
2010 Workshop on Information Theoretic Methods in Science and Engineering, August 16-18, 2010, Tampere, Finland
Available from: 2018-02-06 Created: 2018-02-06 Last updated: 2018-02-06

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Helske, Jouni

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