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Thematic Fuzzy Clusters with an Additive Spectral Approach
2011
NaFeMi11
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.
In proceedings
Susana Nascimento, Rui Felizardo, Boris Mirkin
L. Antunes, H. S. Pinto
Procs. of the 15th Portuguese Conference on Artificial Intelligence
LNCS
Springer
-
7026/2011
446-461
978-3-642-24768-2
-
-
http://www.springerlink.com/content/371068774v12p166/
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Susana Nascimento and Rui Felizardo and Boris Mirkin, Thematic Fuzzy Clusters with an Additive Spectral Approach, in: L. Antunes and H. S. Pinto (eds), Procs. of the 15th Portuguese Conference on Artificial Intelligence, LNCS, Springer, Vol. 7026/2011, ISBN 978-3-642-24768-2, Pag. 446-461, (http://www.springerlink.com/content/371068774v12p166/), 2011.
<a href="/people/members/view.php?code=4d69262d034cb8174d039bea8d970836" class="author">Susana Nascimento</a>, Rui Felizardo and Boris Mirkin, <b>Thematic Fuzzy Clusters with an Additive Spectral Approach</b>, in: L. Antunes and H. S. Pinto (eds), <u>Procs. of the 15th Portuguese Conference on Artificial Intelligence</u>, LNCS, <a href="http://www.springer.com" title="Link to external entity..." target="_blank" class="publisher">Springer</a>, Vol. 7026/2011, ISBN 978-3-642-24768-2, Pag. 446-461, (<a href="http://www.springerlink.com/content/371068774v12p166/" target="_blank">url</a>), 2011.
@inproceedings {NaFeMi11, author = {Susana Nascimento and Rui Felizardo and Boris Mirkin}, editor = {L. Antunes and H. S. Pinto}, title = {Thematic Fuzzy Clusters with an Additive Spectral Approach}, booktitle = {Procs. of the 15th Portuguese Conference on Artificial Intelligence}, series = {LNCS}, publisher = {Springer}, volume = {7026/2011}, pages = {446-461}, isbn = {978-3-642-24768-2}, url = {http://www.springerlink.com/content/371068774v12p166/}, 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.}, year = {2011}, }
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