Publication: Network clustering by graph coloring: An application to astronomical images
Loading...
Full text at PDC
Publication Date
2011
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Citations
Exportar
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.
Description
UCM subjects
Unesco subjects
Keywords
Citation
[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).