Nascimento, S. and Felizardo, R. and Mirkin, Boris (2011) Thematic fuzzy clusters with an additive spectral approach. In: Antunes, L. and Pinto, S.H. (eds.) Progress in Artificial Intelligence. Berlin, Germany: Springer Verlag, pp. 446-461. ISBN 9783642247682.
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.
Metadata
| Item Type: | Book Section |
|---|---|
| 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: | Sarah Hall |
| Date Deposited: | 25 Jul 2013 16:33 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7823 |
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