A new encoding scheme for a bee-inspired optimal data clustering algorithm

Proceedings - 1st BRICS Countries Congress on Computational Intelligence, BRICS-CCI 2013

Volumn , Issue , 2013, Pages 136-141

  Cruz, Davila Patricia Ferreira ^a   Maia, Renato Dourado ^b   De Castro, Leandro Nunes ^a  

^a MACKENZIE PRESBYTERIAN UNIVERSITY   (Brazil)

^b STATE UNIVERSITY OF MONTES CLAROS   (Brazil)

Author keywords

Bee inspired algorithms; Dynamic size population; Optimal data clustering; Swarm intelligence

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; DATA MINING; ENCODING (SYMBOLS);

BEE-INSPIRED ALGORITHMS; DATA-MINING TOOLS; DIVERSITY OF SOLUTIONS; DYNAMIC SIZES; ENCODING SCHEMES; NUMBER OF CLUSTERS; OPTIMAL DATA; SWARM INTELLIGENCE;

CLUSTERING ALGORITHMS;

EID: 84905397802     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BRICS-CCI-CBIC.2013.32     Document Type: Conference Paper

References (15)

1. J. Han and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed. San Francisco: Elsevier, 2006.
2. R. Elmasri and S. B. Navathe, Sistemas de Banco de Dados, 4th ed. São Paulo, Brazil: Pearson, 2006.
3. D. P. F. Cruz, R. D. Maia, L. N. Castro and A. Szabo, "A Bee-Inspired Algorithm for Optimal Data Clustering" in: IEEE World Congr. on Computational Evolutionary, Cancún, México, 2013, pp.3140-3147.
4. R. D. Maia, L. N. Castro and W. M. Caminhas, "Bee Colonies as Model for Multimodal Continuous Optimization: The OptBees Algorithm," in: IEEE World Congress on Computational Intelligence, Brisbane, Australia, 2012, pp. 3316-3323.
5. R. D. Maia, "Colônias de Abelhas como Modelo para Otimização Multimodal em Espaços Contínuos: Uma Abordagem Baseada em Alocacao de Tarefas", doctoral dissertation, Universidade Federal de Minas Gerais, Engenharia Elétrica, Belo Horizonte, 2012.
6. R. D. Maia, W. O. Barbosa and L. N. De Castro, "Colonias de Abelhas como Modelo para Agrupamento Ótimo de Dados: Uma proposta e analise parametrica qualitativa," in: XIX Congresso Brasileiro de Automática, Campina Grande, 2012.
7. J. E. Gadau and J. Fewell, Organization of Insect Societies: From genome to sociocomplexity. HUP, Cambridge, 2009.
8. P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen, A. Auger and S. Tiwari, "Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization", Technical Report, Nanyang Technol. University, Singapore, 2005.
9. UCI Machine Learning Repository, 1998. Available: Http://archive.ics.uci. edu/ml/ [Accessed: Feb. 2013].
10. A. Szabo, L. N. De Castro and M. R. Delgado, "FaiNet: An immune algorithm for fuzzy clustering", in: Prec. IEEE International Conference on Fuzzy Systems, 2012, pp .1-9.
11. P. J. Rousseeuw, "Silhouettes: A graphical aid to the interpretation and validation of cluster analysis", Journal of Computational and Applied Mathematics, vol. 20, pp. 53-65, 1987.
12. E. R. Hruschka, L. N. De Castro and R. J. G. B, Campello, "Evolutionary algorithms for clustering gene-expression data", Prec. IEEE World Congress on DataMining, 2004, pp. 403-406.
13. H. R. Ruspini, "Numerical Methods for Fuzzy Clustering", in: Information Sciences, 1970, pp. 139-350.
14. D. Karoba and C. Ozturk, "A Novel Clustering Approach: Artificial Bee Colony (ABC) Algorithm". Applied Soft Computing, vol. 11, no. 1, pp. 652-657, jan. 2011.
15. J. B. Macqueen, "Some Methods for classification and Analysis of Multivariate Observations". Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, California, 1967, pp. 281-297. .