New methods for the analysis of binarized BIOLOG GN data of Vibrio species: Minimization of stochastic complexity and cumulative classification

Mats Gyllenberg, Timo Koski, Peter Dawyndt, Tatu Lund, Fabiano Thompson, Brian Austin, Jean Swings

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)403-415
    Number of pages13
    JournalSystematic and Applied Microbiology
    Volume25
    Issue number3
    Publication statusPublished - Oct 2002

    Keywords

    • Bacterial taxonomy
    • Cumulative classification
    • Machine learning

    Fingerprint Dive into the research topics of 'New methods for the analysis of binarized BIOLOG GN data of Vibrio species: Minimization of stochastic complexity and cumulative classification'. Together they form a unique fingerprint.

    Cite this