Network clustering by graph coloring: An application to astronomical images
dc.book.title | Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on | |
dc.contributor.author | Zarrazola Rivera, Edwin | |
dc.contributor.author | Montero De Juan, Francisco Javier | |
dc.contributor.author | Yáñez Gestoso, Francisco Javier | |
dc.contributor.author | Gómez De Castro, Ana Inés | |
dc.date.accessioned | 2023-06-20T05:46:31Z | |
dc.date.available | 2023-06-20T05:46:31Z | |
dc.date.issued | 2011 | |
dc.description.abstract | In this paper we propose an efficient and polynomial hierarchical clustering technique for unsupervised classification of items being connected by a graph. The output of this algorithm shows the cluster evolution in a divisive way, in such a way that s soon as two items are included in the same cluster they will join a common cluster until the last iteration, in which all the items belong to a singleton cluster. This output can be viewed as a fuzzy clustering in which for each alpha cut we have a standard cluster of the network. The clustering tool we present in this paper allows a hierarchical clustering of related items avoiding some unrealistic constraints that are quite often assumed in clustering problems. The proposed procedure is applied to a hierarchical segmentation problem in astronomical images. | en |
dc.description.department | Depto. de Estadística e Investigación Operativa | |
dc.description.faculty | Fac. de Ciencias Matemáticas | |
dc.description.refereed | TRUE | |
dc.description.status | pub | |
dc.eprint.id | https://eprints.ucm.es/id/eprint/28655 | |
dc.identifier.doi | 10.1109/ISDA.2011.6121754 | |
dc.identifier.isbn | 978-1-4577-1676-8 | |
dc.identifier.officialurl | https//doi.org/10.1109/ISDA.2011.6121754 | |
dc.identifier.relatedurl | http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6121754&abstractAccess=no&userType=inst | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/45541 | |
dc.language.iso | eng | |
dc.page.final | 801 | |
dc.page.initial | 796 | |
dc.page.total | 1402 | |
dc.publisher | IEEE | |
dc.relation.projectID | TIN2009-07901 | |
dc.rights.accessRights | restricted access | |
dc.subject.cdu | 519.8 | |
dc.subject.keyword | Graph Theory | |
dc.subject.keyword | Hierarchical Clustering | |
dc.subject.keyword | Astronomical Images. | |
dc.subject.ucm | Investigación operativa (Matemáticas) | |
dc.subject.unesco | 1207 Investigación Operativa | |
dc.title | Network clustering by graph coloring: An application to astronomical images | en |
dc.type | book part | |
dcterms.references | [1] S. Fortunato. “Community detection in graphs”. Physics Reports 486,75–174 (2010). [2] M. Girvan and M.E.J. Newman. “Community structure in social and biological networks”. Proceedings of the National Academy of Sciences of the USA 99, 7821–7826 (2002). [3] D. Gomez and J. Montero. “Fuzzy sets in remote sensing classification”. Soft Computing 12, 243–249 (2008). [4] D. Gomez, J. Montero and G. Biging. “Improvements to remote sensing using fuzzy classification, graphs and accuracy statistics”. Pure and Applied Geophysics 165, 1555–1575 (2008). [5] D. Gomez, J. Montero and G. Biging. “Accuracy statistics for judging soft classification”. International Journal of Remote Sensing 29(3), 693–709 (2008). [6] D. G´omez, J. Montero and J. Y´anez. “A coloring algorithm for image classification”. Information Sciences 176, 3645–3657 (2006). [7] D. Gomez, J. Montero, J. Yañez and C. Poidomani. “A graph coloring algorithm approach for image segmentation”. Omega 35, 173–183 (2007). [8] W. Groissboeck, E. Lughofer and S. Thumfart. “Associating visual textures with human perceptions using genetic algorithms”. Information Sciences 180(11), 2065–2084 (2010). [9] R. Kruse. “Temporal aspects in data mining”. Proceedings of the 2010 World Congress on Computational Intelligence, Barcelona, 18–23 July (IEEE Press, 2010). [10] P. Larraˆnaga. “Probabilistic graphical models and evolutionary computation”. Proceedings of the 2010 World Congress on Computational Intelligence, Barcelona, 18–23 July (IEEE Press, 2010). [11] C. Martin, T. Barlow, W. Barnhart, L. et al. “The Galaxy Evolution Explorer”. Society of Photo-Optical Instrumentation Engineers (SPIE)Conference Series, 336–350 February 2003. [12] S. Mitra. “Hybridization with rough sets”. Proceedings of the 2010 World Congress on Computational Intelligence, Barcelona, 18–23 July (IEEE Press, 2010). [13] J. Montero, V. Lopez and D. G´omez. “The role of fuzziness in decision making”. Studies in Fuzziness and Soft Computing 215, 337–349 (2007). [14] J. Montero and L. Martınez. “Upgrading ideas about the concept of soft computing”. International Journal of Computational Intelligence Systems 3, 144–147 (2010). [15] M.E.J. Newman. “Analysis of weighted networks”. Physical Review E 70, 056131 (2004). [16] E. Ruspini. “From clusters to models and perceptions: The evolution of fuzzy clustering”. Proceedings of the 2010 World Congress on Computational Intelligence, Barcelona, 18–23 July (IEEE Press, 2010). [17] F. Tavares-Pereira, J.R. Figueira, V. Mousseau and B. Roy. “Multiple criteria districting problems – The public transportation network pricing system of the Paris region”. Annals of Operations Research 154, 69–92 (2007). [18] S. Usui. “PLATO: Platform for collaborative brain system modeling”. Proceedings of the 2010 World Congress on Computational Intelligence,Barcelona, 18–23 July (IEEE Press, 2010). [19] J.Yañez, S. Muñoz and J. Montero. “Graph coloring inconsistencies in image segmentation”. Computer Engineering and Information Sciences 1, 435–440 (2008). [20] S. Yue, J.S. Wang, T. Wu and H. Wang. “A new separation measure for improving the effectiveness of validity indices. Information Sciences 180(5), 748–764 (2010). | |
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