Caracterização de Classes via Otimização em Redes Complexas
Resumo
As redes complexas surgiram como uma importante maneira de representação e abstração de dados capaz de capturar as relações topológicas presentes em bases de dados. Este trabalho propõe um método para construção de rede a partir de uma base de dados vetorial, o qual é baseado na otimização de uma função de energia que considera medidas de pureza e extensão da rede. A rede construída foi utilizada para caracterizar mistura entre classes de dados em problema de classificação de dados. A caracterização de classes é uma questão importante na classificação de dados, porém ainda é pouco estudada. Desta forma, consideramos este trabalho uma contribuição nesta direção.Referências
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Baldi, P. e Brunak, S. (1998) Bioinformatics: the machine learning approach, MIT Press.
Barabási, A. L. e Albert, R. (1999) “Emergence of scaling in random networks”, Science 286, p. 509-512.
Belkin, M. Niyogi, P. (2003) “Laplacian eigenmaps for dimensionality reduction and data representation”, Neural Computation 15, p. 1373-1396.
Boccaletti, S., Ivanchenko, M., Latora, V., Pluchino, A. e Rapisarda, A. (2007) “Detecting complex network modularity by dynamical clustering”, Physical Review E 75, p. 1-4.
Bornholdt, S. e Schuster, H. G. (2003) Handbook of Graphs and Networks: From the Genome to the Internet, Wiley-VCH.
Cancho, F. e Solé, R. V. (2003) “Optimization in complex networks. Statistical Mechanics of Complex Networks”, Lecture Notes in Physics 625, p. 114-125.
Cook, D. J. e Holder, L. B. (2000) “Graph-based data mining”, IEEE Intelligent Systems 15, p. 32-41.
Erdös, P. e Rényi, A. (1959) “On random graphs”, Publicationes Mathematicae 6, p. 290-297.
Faloutsos, M., Faloutsos, P. e Faloutsos, C. (1999) “On power-law relationship of the internet topology”, Computer Communication Review 29, p. 251-262.
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. e Jackel, L. D. (1989) “Backpropagation applied to handwritten zip code recognition”, Neural Computation 1, p. 541-551.
Lopes, A. A., Bertini, Jr. J. R., Motta, R. e Zhao, L. (2009) “Classification Based on the Optimal K-Associated Network”, Proceedings of The First International Conference on Complex Sciences: Theory and Applications, p. 1-11.
Mitchell, T. M. (1997) Machine learning. McGraw-Hill Series in Computer Science, McGrawHill.
Newman, M. E. J. (2004) “Fast algorithm for detecting community structure in networks”, Physical Review E 69 066133.
Newman, M. E. J. e Girvan, M. (2004) “Finding and evaluating community structure in networks”, Physical Review E 69 026113.
Quiles, M. G., Zhao, L., Alonso, R. L. e Romero, R. A. F. (2008) “Particle competition for complex network community detection”, Chaos 18, p. 1-10.
Reichardt, J. e Bornholdt, S. (2004) “Detecting fuzzy community structures in complex networks with a Potts model” Physical Review Letters 93, p. 1-4.
Scott, J. (2000) Social network analysis: a handbook, Sage.
Sponrs, O. (2002) “Networks analysis, complexity, and brain function”, Complexity 8, p. 56-60.
Von Luxburg, U. (2007) “A tutorial on spectral clustering”, Statistical Computation 17, p. 395416.
Watts, D. J. e Strogatz, S. H. (1998) “Collective dynamics of „small-world‟ networks”, Nature 393, p. 440-442.
Wolpert, D. H. e Macready, W. G. (1997) No Free Lunch Theorems for Optimization, IEEE Transactions on Evolutionary Computation 1, p. 67.
Zhou, H. (2003a) “Network landscape from a Brownian particlés perspective”. Physical Review E 67 041908.
Zhou, H. (2003b) “Distance, dissimilarity index, and network community structure”. Physical Review E 67 061901.
Zhu, X. (2005) “Semi-supervised learning literature review”. Technical Reporter 1530, Computer-Science, University of Wisconsin-Madison.
Baldi, P. e Brunak, S. (1998) Bioinformatics: the machine learning approach, MIT Press.
Barabási, A. L. e Albert, R. (1999) “Emergence of scaling in random networks”, Science 286, p. 509-512.
Belkin, M. Niyogi, P. (2003) “Laplacian eigenmaps for dimensionality reduction and data representation”, Neural Computation 15, p. 1373-1396.
Boccaletti, S., Ivanchenko, M., Latora, V., Pluchino, A. e Rapisarda, A. (2007) “Detecting complex network modularity by dynamical clustering”, Physical Review E 75, p. 1-4.
Bornholdt, S. e Schuster, H. G. (2003) Handbook of Graphs and Networks: From the Genome to the Internet, Wiley-VCH.
Cancho, F. e Solé, R. V. (2003) “Optimization in complex networks. Statistical Mechanics of Complex Networks”, Lecture Notes in Physics 625, p. 114-125.
Cook, D. J. e Holder, L. B. (2000) “Graph-based data mining”, IEEE Intelligent Systems 15, p. 32-41.
Erdös, P. e Rényi, A. (1959) “On random graphs”, Publicationes Mathematicae 6, p. 290-297.
Faloutsos, M., Faloutsos, P. e Faloutsos, C. (1999) “On power-law relationship of the internet topology”, Computer Communication Review 29, p. 251-262.
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. e Jackel, L. D. (1989) “Backpropagation applied to handwritten zip code recognition”, Neural Computation 1, p. 541-551.
Lopes, A. A., Bertini, Jr. J. R., Motta, R. e Zhao, L. (2009) “Classification Based on the Optimal K-Associated Network”, Proceedings of The First International Conference on Complex Sciences: Theory and Applications, p. 1-11.
Mitchell, T. M. (1997) Machine learning. McGraw-Hill Series in Computer Science, McGrawHill.
Newman, M. E. J. (2004) “Fast algorithm for detecting community structure in networks”, Physical Review E 69 066133.
Newman, M. E. J. e Girvan, M. (2004) “Finding and evaluating community structure in networks”, Physical Review E 69 026113.
Quiles, M. G., Zhao, L., Alonso, R. L. e Romero, R. A. F. (2008) “Particle competition for complex network community detection”, Chaos 18, p. 1-10.
Reichardt, J. e Bornholdt, S. (2004) “Detecting fuzzy community structures in complex networks with a Potts model” Physical Review Letters 93, p. 1-4.
Scott, J. (2000) Social network analysis: a handbook, Sage.
Sponrs, O. (2002) “Networks analysis, complexity, and brain function”, Complexity 8, p. 56-60.
Von Luxburg, U. (2007) “A tutorial on spectral clustering”, Statistical Computation 17, p. 395416.
Watts, D. J. e Strogatz, S. H. (1998) “Collective dynamics of „small-world‟ networks”, Nature 393, p. 440-442.
Wolpert, D. H. e Macready, W. G. (1997) No Free Lunch Theorems for Optimization, IEEE Transactions on Evolutionary Computation 1, p. 67.
Zhou, H. (2003a) “Network landscape from a Brownian particlés perspective”. Physical Review E 67 041908.
Zhou, H. (2003b) “Distance, dissimilarity index, and network community structure”. Physical Review E 67 061901.
Zhu, X. (2005) “Semi-supervised learning literature review”. Technical Reporter 1530, Computer-Science, University of Wisconsin-Madison.
Publicado
19/07/2011
Como Citar
BERTON, Lilian; ZHAO, Liang.
Caracterização de Classes via Otimização em Redes Complexas. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 8. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 548-559.
ISSN 2763-9061.