Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/7244
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: New methods for the analysis of binarized BIOLOG GN data of vibrio species: Minimization of stochastic complexity and cumulative classification
Authors: Gyllenberg, Mats
Koski, Timo
Dawyndt, Peter
Lund, Tatu
Thompson, Fabiano L
Austin, Brian
Swings, Jean
Contact Email: brian.austin@stir.ac.uk
Keywords: bacterial taxonomy
machine learning
cumulative classification
Issue Date: Oct-2002
Publisher: Elsevier
Citation: Gyllenberg M, Koski T, Dawyndt P, Lund T, Thompson FL, Austin B & Swings J (2002) New methods for the analysis of binarized BIOLOG GN data of vibrio species: Minimization of stochastic complexity and cumulative classification, Systematic and Applied Microbiology, 25 (3), pp. 403-415.
Abstract: We apply minimization of stochastic complexity and the closely related method of cumulative classification to analyse the extensively studied BIOLOG GN data of Vibrio spp. Minimization of stochastic complexity provides an objective tool of bacterial taxonomy as it produces classifications that are optimal from the point of view of information theory. We compare the outcome of our results with previously published classifications of the same data set. Our results both confirm earlier detected relationships between species and discover new ones.
Type: Journal Article
URI: http://hdl.handle.net/1893/7244
DOI Link: http://dx.doi.org/10.1078/0723-2020-00109
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: University of Turku
Linkoping University
University of Turku
Ghent University
Heriot-Watt University
Ghent University

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