A hybrid cluster-lift method for the analysis of research activities

Authors:
Boris Mirkin;Susana Nascimento;Trevor Fenner;Luís Moniz Pereira
Affiliations:
School of Computer Science, Birkbeck University of London, London, UK;Computer Science Department and Centre for Artificial Intelligence (CENTRIA), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal;School of Computer Science, Birkbeck University of London, London, UK;Computer Science Department and Centre for Artificial Intelligence (CENTRIA), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

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Abstract

A hybrid of two novel methods - additive fuzzy spectral clustering and lifting method over a taxonomy - is applied to analyse the research activities of a department To be specific, we concentrate on the Computer Sciences area represented by the ACM Computing Classification System (ACM-CCS), but the approach is applicable also to other taxonomies Clusters of the taxonomy subjects are extracted using an original additive spectral clustering method involving a number of model-based stopping conditions The clusters are parsimoniously lifted then to higher ranks of the taxonomy by minimizing the count of “head subjects” along with their “gaps” and “offshoots” An example is given illustrating the method applied to real-world data.