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    Cluster-lift method for mapping research activities over a concept tree

    Mirkin, Boris and Nascimento, S. and Pereira, L.M. (2010) Cluster-lift method for mapping research activities over a concept tree. Studies in Computational Intelligence 263 , pp. 245-257. ISSN 1860-949X.

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    The paper builds on the idea by R. Michalski of inferential concept interpretation for knowledge transmutation within a knowledge structure taken here to be a concept tree. We present a method for representing research activities within a research organization by doubly generalizing them. To be specific, we concentrate on the Computer Sciences area represented by the ACM Computing Classification System (ACM-CCS). Our cluster-lift method involves two generalization steps: one on the level of individual activities (clustering) and the other on the concept structure level (lifting). Clusters are extracted from the data on similarity between ACM-CCS topics according to the working in the organization. Lifting leads to conceptual generalization of the clusters in terms of “head subjects” on the upper levels of ACM-CCS accompanied by their gaps and offshoots. A real-world example of the representation is provided.


    Item Type: Article
    Keyword(s) / Subject(s): Cluster-lift method, additive clustering, concept generalization, concept tree, knowledge transmutation
    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: Administrator
    Date Deposited: 01 Feb 2011 15:03
    Last Modified: 09 Aug 2023 12:30


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