Improving Group Search Optimization Through Local Search Heuristics for Automatic Data Clustering
Resumo
Neste trabalho, três models de Agrupamento Automático de Dados, baseados na meta-heurística de Otimização por Busca em Grupo (GSO), são introduzidos, chamados RHGSO, ADHGSO e BDHGSO. Nos modelos propostos, a busca global do GSO é melhorada através de heurísticas de busca local adaptadas ao contexto de Agrupamento Automático de Dados, onde operações de ativação, desativação e substituição de centroides de agrupamentos são executadas, objetivando a realização de perturbações que visam o aumento da velocidade de exploração do grupo do GSO. Os algoritmos propostos são comparados a outros Algoritmos Evolucionários e de Inteligência de Enxames da literatura, apresentando resultados promissores.
Referências
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Halkidi, M., Batistakis, Y., and Vazirgiannis, M. (2002). Cluster validity methods: part i. ACM Sigmod Record, 31(2):40-45.
He, S., Wu, Q. H., and Saunders, J. R. (2009). Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 13(5):973-990.
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Pacifico, L. and Ludermir, T. (2020). Backtracking group search optimization: A hybrid approach for automatic data clustering. In Brazilian Conference on Intelligent Systems, pages 64-78. Springer.
Pacifico, L. D. and Ludermir, T. B. (2021). An evaluation of k-means as a local search operator in hybrid memetic group search optimization for data clustering. Natural Computing, 20(3):611-636.
Preetha, V. (2021). Data analysis on student's performance based on health status using genetic algorithm and clustering algorithms. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pages 836-842. IEEE.
Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association, 66(336):846-850.
Shi, X., Zhang, X., and Xu, M. (2020). A self-adaptive preferred learning differential evolution algorithm for task scheduling in cloud computing. In 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), pages 145-148. IEEE.
Storn, R. and Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. international computer science institute, berkeley. Technical report, CA, 1995, Tech. Rep. TR-95-012.
Tam, H.-H., Ng, S.-C., Lui, A. K., and Leung, M.-F. (2017). Improved activation schema on automatic clustering using differential evolution algorithm. In 2017 IEEE Congress on Evolutionary Computation (CEC), pages 1749-1756. IEEE.
Ye, L. and Zheng, D. (2021). Stable grasping control of robot based on particle swarm optimization. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), pages 1020-1024. IEEE.
CalinÌski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1):1-27.
Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and computation, 219(15):8121-8144.
Das, S., Abraham, A., and Konar, A. (2007). Automatic clustering using an improved differential evolution algorithm. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans, 38(1):218-237.
Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. IEEE transactions on pattern analysis and machine intelligence, (2):224-227.
Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7:1-30.
Dorigo, M., Maniezzo, V., and Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26(1):29-41.
Ezugwu, A. E., Shukla, A. K., Agbaje, M. B., Oyelade, O. N., José-García, A., and Agushaka, J. O. (2021). Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature. Neural Computing and Applications, 33(11):6247-6306.
Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the american statistical association, 32(200):675-701.
Halkidi, M., Batistakis, Y., and Vazirgiannis, M. (2002). Cluster validity methods: part i. ACM Sigmod Record, 31(2):40-45.
He, S., Wu, Q. H., and Saunders, J. R. (2009). Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 13(5):973-990.
Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1):66-72.
Hubert, L. and Arabie, P. (1985). Comparing partitions. Journal of classification, 2(1):193-218.
Ikotun, A. M., Almutari, M. S., and Ezugwu, A. E. (2021). K-means-based nature-inspired metaheuristic algorithms for automatic data clustering problems: Recent advances and future directions. Applied Sciences, 11(23):11246.
José-García, A. and Gómez-Flores, W. (2016). Automatic clustering using nature-inspired metaheuristics: A survey. Applied Soft Computing, 41:192-213.
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pages 1942-1948. IEEE.
Nemenyi, P. (1962). Distribution-free multiple comparisons. In Biometrics, volume 18, page 263. INTERNATIONAL BIOMETRIC SOC 1441 I ST, NW, SUITE 700, WASHINGTON, DC 20005-2210.
Pacífico, L. (2020). Agrupamento de imagens baseado em uma abordagem híbrida entre a otimização por busca em grupo e k-means para a segmentação automática de doenças em plantas. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional, pages 152-163. SBC.
Pacifico, L. and Ludermir, T. (2020). Backtracking group search optimization: A hybrid approach for automatic data clustering. In Brazilian Conference on Intelligent Systems, pages 64-78. Springer.
Pacifico, L. D. and Ludermir, T. B. (2021). An evaluation of k-means as a local search operator in hybrid memetic group search optimization for data clustering. Natural Computing, 20(3):611-636.
Preetha, V. (2021). Data analysis on student's performance based on health status using genetic algorithm and clustering algorithms. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pages 836-842. IEEE.
Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association, 66(336):846-850.
Shi, X., Zhang, X., and Xu, M. (2020). A self-adaptive preferred learning differential evolution algorithm for task scheduling in cloud computing. In 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), pages 145-148. IEEE.
Storn, R. and Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. international computer science institute, berkeley. Technical report, CA, 1995, Tech. Rep. TR-95-012.
Tam, H.-H., Ng, S.-C., Lui, A. K., and Leung, M.-F. (2017). Improved activation schema on automatic clustering using differential evolution algorithm. In 2017 IEEE Congress on Evolutionary Computation (CEC), pages 1749-1756. IEEE.
Ye, L. and Zheng, D. (2021). Stable grasping control of robot based on particle swarm optimization. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), pages 1020-1024. IEEE.
Publicado
28/11/2022
Como Citar
PACÍFICO, Luciano D. S.; LUDERMIR, Teresa B..
Improving Group Search Optimization Through Local Search Heuristics for Automatic Data Clustering. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 118-129.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2022.227578.
