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    Constructing and mapping fuzzy thematic clusters to higher ranks in a taxonomy

    Mirkin, Boris and Nascimento, S. and Fenner, Trevor and Pereira, L.M. (2010) Constructing and mapping fuzzy thematic clusters to higher ranks in a taxonomy. In: Bi, Y. and Williams, M.A. (eds.) Knowledge Science, Engineering and Management. Lecture Notes in Computer Science 6291. Berlin, Germany: Springer Verlag, pp. 329-340. ISBN 9783642152801.

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    Abstract

    We present a novel methodology for mapping a system such as a research department to a related taxonomy in a thematically consistent way. The components of the structure are supplied with fuzzy membership profiles over the taxonomy. Our method generalizes the profiles in two steps: first, by fuzzy clustering, and then by mapping the clusters to higher ranks of the taxonomy. To be specific, we concentrate on the Computer Sciences area represented by the taxonomy of ACM Computing Classification System (ACM-CCS). We build fuzzy clusters of the taxonomy leaves according to the similarity between individual profiles by using a novel, additive spectral, fuzzy clustering method that, in contrast to other methods, involves a number of model-based stopping conditions. The clusters are not necessarily consistent with the taxonomy. This is formalized by a novel method for parsimoniously elevating them to higher ranks of the taxonomy using an original recursive algorithm for minimizing a penalty function that involves “head subjects” on the higher ranks of the taxonomy along with their “gaps” and “offshoots”. An example is given illustrating the method applied to real-world data.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Structural Molecular Biology, Institute of (ISMB)
    Depositing User: Sarah Hall
    Date Deposited: 16 May 2013 16:37
    Last Modified: 06 Dec 2016 10:33
    URI: http://eprints.bbk.ac.uk/id/eprint/6816

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