Aplicação de conceitos de redes complexas para a descoberta de formação de grupos em mapas auto-organizáveis
Resumo
Redes Neurais do tipo Mapas Auto-Organizáveis ou SOM (do inglês Self-Organizing Maps), em particular, se destaca como um dos algoritmos de agrupamento por permitir analisar as características de agrupamento e a relação topológica entre grupos a partir de um reticulado de neurônios. Contudo, ainda há uma lacuna de pesquisa, que consiste em descobrir a relação por de trás dos atributos que levam a formação de grupos. Neste sentido, propõe-se neste trabalho o uso de conceitos de redes complexas no sentido de usar os neurônios do reticulado para a geração de um grafo e complementar a análise no contexto de comunidade, analisando a formação de grupos por medidas de centralidade.
Palavras-chave:
mapas autoorganizaveis, sitemas complexos, agrupamentos, medidas centralidades, redes neurais
Referências
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Borgatti, S. P., E. M. G. (1999). The centrality of groups and classes. Journal of Mathematical Sociology, 23:181–201.
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Grassi, R., S. S. T. A. (2009). Centrality in organizational networks. International Journal of Intelligent Systems, 25:253–265.
Jain (2010). Data clustering: 50 years beyond K-Means. Pattern Recognition Letters, 31(8):651–666.
Kitani, E. C., Del-Moral-Hernandez, E., and Silva, L. A. (2012). Somm–self-organized manifold mapping. In International Conference on Artificial Neural Networks, pages 355–362. Springer.
Kitani, E. C., Del-Moral-Hernandez, E., and Silva, L. A. (2013). Learning embedded data structure with self-organizing maps. In Advances in Self-Organizing Maps, pages 225–234. Springer.
Kohonen, T. (2001). Self-Organizing Maps. Third extended. Springer, Heidelberg.
Kohonen, T. (2013). Essentials of the self-organizing map. Neural networks, 37:52–65.
Mariette, J., Rossi, F., Olteanu, M., and Villa-Vialaneix, N. (2017). Accelerating stochas- tic kernel som. In M., V., editor, Proceedings of XXVth European Symposium on Ar- tificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), pages 269–274. i6doc
Monteiro, L. H. A. (2014). Sistemas Dinaˆmicos Complexos., volume 2. Editora Livraria da Física.
Newan, M. E. J. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27:39–54.
Olteanu, M. and Villa-Vialaneix, N. (2015). On-line relational and multiple relational som. Neurocomputing, 147:15–30.
Olteanu, M., Villa-Vialaneix, N., and Cottrell, M. (2012). On-line relational som for dis- similarity data. In P., E., J., P., P., Z., and G., B., editors, Advances in Self-Organizing Maps (Proceedings of WSOM 2012, Santiago, Chili, 12-14 decembre 2012), volume 198 of Advances in Intelligent Systems and Computing series, pages 13–22, Ber- lin/Heidelberg. Springer Verlag.
Pei, J. H. M. K. J. (2011). Data Mining: Concepts and Techniques. Elsevier, Waltham, MA, USA.
Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., Er, M. J., Ding, W., and Lin, C.-T. (2017). A review of clustering techniques and developments. Neu- rocomputing, 267:664–681.
Silva, L. A. and Costa, J. A. F. (2011). A graph partitioning approach to som clustering. In International Conference on Intelligent Data Engineering and Automated Learning, pages 152–159. Springer.
Strogatz, S. H. (2001). Exploring complex networks. nature. Nature Publishing Group, v. 410:p.268–276.
Vesanto, J. and Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Tran- sactions on neural networks, 11(3):586–600.
Z. Wu, R. L. (1993). An optimal graph theoretic approach to data clustering: theory ans its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1101–1113.
Barabasi, A.-L.; Jeong, H. N. Z. R. E. S. A. V. T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, v. 311:p.590–614.
Boaventura Netto, P. O. (2001). Grafos: Teoria, Modelos, Algoritmos., volume 2. Editora Blucher.
Borgatti, S. P., E. M. G. (1999). The centrality of groups and classes. Journal of Mathematical Sociology, 23:181–201.
Csardi, G. and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems:1695.
Dua, D. and Graff, C. (2017). UCI machine learning repository.
Grando, F. (2015). On the analysis of centrality measures for complex and social networks. http://hdl.handle.net/10183/122516.
Grassi, R., S. S. T. A. (2009). Centrality in organizational networks. International Journal of Intelligent Systems, 25:253–265.
Jain (2010). Data clustering: 50 years beyond K-Means. Pattern Recognition Letters, 31(8):651–666.
Kitani, E. C., Del-Moral-Hernandez, E., and Silva, L. A. (2012). Somm–self-organized manifold mapping. In International Conference on Artificial Neural Networks, pages 355–362. Springer.
Kitani, E. C., Del-Moral-Hernandez, E., and Silva, L. A. (2013). Learning embedded data structure with self-organizing maps. In Advances in Self-Organizing Maps, pages 225–234. Springer.
Kohonen, T. (2001). Self-Organizing Maps. Third extended. Springer, Heidelberg.
Kohonen, T. (2013). Essentials of the self-organizing map. Neural networks, 37:52–65.
Mariette, J., Rossi, F., Olteanu, M., and Villa-Vialaneix, N. (2017). Accelerating stochas- tic kernel som. In M., V., editor, Proceedings of XXVth European Symposium on Ar- tificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), pages 269–274. i6doc
Monteiro, L. H. A. (2014). Sistemas Dinaˆmicos Complexos., volume 2. Editora Livraria da Física.
Newan, M. E. J. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27:39–54.
Olteanu, M. and Villa-Vialaneix, N. (2015). On-line relational and multiple relational som. Neurocomputing, 147:15–30.
Olteanu, M., Villa-Vialaneix, N., and Cottrell, M. (2012). On-line relational som for dis- similarity data. In P., E., J., P., P., Z., and G., B., editors, Advances in Self-Organizing Maps (Proceedings of WSOM 2012, Santiago, Chili, 12-14 decembre 2012), volume 198 of Advances in Intelligent Systems and Computing series, pages 13–22, Ber- lin/Heidelberg. Springer Verlag.
Pei, J. H. M. K. J. (2011). Data Mining: Concepts and Techniques. Elsevier, Waltham, MA, USA.
Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., Er, M. J., Ding, W., and Lin, C.-T. (2017). A review of clustering techniques and developments. Neu- rocomputing, 267:664–681.
Silva, L. A. and Costa, J. A. F. (2011). A graph partitioning approach to som clustering. In International Conference on Intelligent Data Engineering and Automated Learning, pages 152–159. Springer.
Strogatz, S. H. (2001). Exploring complex networks. nature. Nature Publishing Group, v. 410:p.268–276.
Vesanto, J. and Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Tran- sactions on neural networks, 11(3):586–600.
Z. Wu, R. L. (1993). An optimal graph theoretic approach to data clustering: theory ans its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1101–1113.
Publicado
18/07/2021
Como Citar
ROLEMBERG, Thiago; SILVA, Leandro A..
Aplicação de conceitos de redes complexas para a descoberta de formação de grupos em mapas auto-organizáveis. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 10. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 1-12.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2021.16120.
