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    How to visualize a crisp or fuzzy topic set over a taxonomy

    Mirkin, Boris and Nascimento, S. and Fenner, Trevor and Felizardo, R. (2011) How to visualize a crisp or fuzzy topic set over a taxonomy. In: Kuznetsov, S.O. and Mandal, D.P. and Kundu, M.K. and Pal, S.K (eds.) Pattern Recognition and Machine Intelligence. Lecture Notes in Computer Science 6744 6744. Berlin, Germany: Springer Verlag, pp. 3-12. ISBN 9783642217869.

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    A novel method for visualization of a fuzzy or crisp topic set is developed. The method maps the set’s topics to higher ranks of the taxonomy tree of the field. The method involves a penalty function summing penalties for the chosen “head subjects” together with penalties for emerging “gaps” and “offshoots”. The method finds a mapping minimizing the penalty function in recursive steps involving two different scenarios, that of ‘gaining a head subject’ and that of ‘not gaining a head subject’. We illustrate the method by applying it to illustrative and real-world data.


    Item Type: Book Section
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Structural Molecular Biology, Institute of (ISMB)
    Depositing User: Sarah Hall
    Date Deposited: 16 May 2013 10:54
    Last Modified: 09 Aug 2023 12:33


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