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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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Official URL: http://dx.doi.org/10.1007/978-3-642-05179-1_12

Abstract

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 or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Administrator
Date Deposited: 01 Feb 2011 15:03
Last Modified: 17 Apr 2013 12:18
URI: http://eprints.bbk.ac.uk/id/eprint/1897

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