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Laplacian Normalization for Deriving Thematic Fuzzy Clusters with an Additive Spectral Approach
May 2013
NaFeMi13
This paper presents a further investigation into computational properties of a novel fuzzy additive spectral clustering method, FADDIS, recently introduced by authors (Mirkin and Nascimento 2012). Specifically, we extend our analysis to "difficult" data structures from the recent literature and develop two synthetic data generators simulating affinity data of Gaussian clusters and genuine additive similarity data, with a controlled level of noise. The FADDIS is experimentally verified on these data in comparison with two state-of-the art fuzzy clustering methods. The claimed ability of FADDIS to help in determining the right number of clusters is experimentally tested and the role of the pseudo-inverse Laplacian data transformation in this is highlighted. A potentially useful extension of the method to biclustering is introduced.
Journal
Susana Nascimento, Rui Felizardo, Boris Mirkin
Expert Systems
Wiley
-
30
4
294-305
-
-
doi: 10.1111/exsy.12027
http://onlinelibrary.wiley.com/doi/10.1111/exsy.12027/abstract
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Susana Nascimento and Rui Felizardo and Boris Mirkin, Laplacian Normalization for Deriving Thematic Fuzzy Clusters with an Additive Spectral Approach, Expert Systems, Vol. 30, No. 4, Pag. 294-305, Wiley, <i>doi: 10.1111/exsy.12027</i>, (http://onlinelibrary.wiley.com/doi/10.1111/exsy.12027/abstract), May 2013.
<b><a href="/people/members/view.php?code=4d69262d034cb8174d039bea8d970836" class="author">Susana Nascimento</a>, Rui Felizardo and Boris Mirkin</b>, <u>Laplacian Normalization for Deriving Thematic Fuzzy Clusters with an Additive Spectral Approach</u>, Expert Systems, Vol. 30, No. 4, Pag. 294-305, <a href="http://www.wiley.com" title="Link to external entity..." target="_blank" class="publisher">Wiley</a>, <i>doi: 10.1111/exsy.12027</i>, (<a href="http://onlinelibrary.wiley.com/doi/10.1111/exsy.12027/abstract" target="_blank">url</a>), May 2013.
@article {NaFeMi13, author = {Susana Nascimento and Rui Felizardo and Boris Mirkin}, title = {Laplacian Normalization for Deriving Thematic Fuzzy Clusters with an Additive Spectral Approach}, journal = {Expert Systems}, publisher = {Wiley}, volume = {30}, number = {4}, pages = {294-305}, note = {doi: 10.1111/exsy.12027}, url = {http://onlinelibrary.wiley.com/doi/10.1111/exsy.12027/abstract}, abstract = {This paper presents a further investigation into computational properties of a novel fuzzy additive spectral clustering method, FADDIS, recently introduced by authors (Mirkin and Nascimento 2012). Specifically, we extend our analysis to "difficult" data structures from the recent literature and develop two synthetic data generators simulating affinity data of Gaussian clusters and genuine additive similarity data, with a controlled level of noise. The FADDIS is experimentally verified on these data in comparison with two state-of-the art fuzzy clustering methods. The claimed ability of FADDIS to help in determining the right number of clusters is experimentally tested and the role of the pseudo-inverse Laplacian data transformation in this is highlighted. A potentially useful extension of the method to biclustering is introduced.}, keywords = {Relational fuzzy clustering; spectral fuzzy clustering; Laplacian normalization; number of clusters}, month = {May}, year = {2013}, }
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