Thematic fuzzy clusters with an additive spectral approach

Authors:
Susana Nascimento;Rui Felizardo;Boris Mirkin
Affiliations:
Department of Computer Science and Centre for Artificial Intelligence, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal;Department of Computer Science and Centre for Artificial Intelligence, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal;Department of Computer Science, Birkbeck University of London, London, UK and School of Applied Mathematics and Informatics, Higher School of Economics, Moscow, RF
Venue:
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Year:
2011

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Abstract

This paper introduces an additive fuzzy clustering model for similarity data as oriented towards representation and visualization of activities of research organizations in a hierarchical taxonomy of the field. We propose a one-by-one cluster extracting strategy which leads to a version of spectral clustering approach for similarity data. The derived fuzzy clustering method, FADDIS, is experimentally verified both on the research activity data and in comparison with two state-of-the-art fuzzy clustering methods. Two developed simulated data generators, affinity data of Gaussian clusters and genuine additive similarity data, are described, and comparison of the results over this data are reported.