Abstract
Ant based algorithms have proved to be very efficient for solving real problems. These algorithms emphasize flexibility, robustness and decentralized control. Thus more and more researches are interested in this new way of designing intelligent systems in which centralization, control and preprogramming are replaced with self-organization, emergence and autonomy. In this context, many ant based algorithms have been proposed for data clustering problem. The purpose of this paper is to present a new intelligent approach for data clustering problem based on social insect metaphor and FCM algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Cliff, D., Husbands, P., Meyer, J., Wilson, S.W. (eds.) Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pp. 501–508. MIT Press (1994)
Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting: robot-like ant and ant-like robot. In: Proceedings of First Conference on Simulation of Adaptative Behavior: From Animals to Animats, pp. 356–365 (1991)
Monmarché, N.: On data clustering with artficial ants. In: Freitas, A.A. (ed.) AAAI 1999 & GECCO 1999 Workshop on Data Mining with Evolutionary Algorithms: Research Directions, pp. 23–26 (1999)
Hamdi, A., Monmarché, N., Slimane, M., Alimi, A.M.: FlyAntClass: intelligent move for ants based clustering algorithm. In: The 13th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2016), Agadir, Morocco (2016)
Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2013). http://archive.ics.uci.edu/ml
Hamdi, A., Monmarché, N., Alimi, M.A., Slimane, M.: SwarmClass: a novel data clustering approach by a hybridization of an ant colony with flying insects. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 411–412. Springer, Heidelberg (2008). doi:10.1007/978-3-540-87527-7_50
Davies, D. L., Bouldin, D. W.: A cluster separation Measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 1(2), 224–227 (1979)
Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3(1), 1–27 (1974)
Hamdi, A., Antoine, V., Monmarché, N., Slimane, M., Alimi, A.M.: Artificial ants for automatic classification. In: Monmarché, N., Guinand, F., Siarry, P. (eds.) Artificial Ants: From Collective Intelligence to Real-life Optimization and Beyond, pp. 265–290. ISTE-WILEY (2010)
Omran, M., Salman, A., Engelbrecht, A.: Image classification using particle swarm optimization. In: Conference on Simulated Evolution and Learning, Singapore, vol. 1, pp. 370–374 (2002)
Karaboga, D., Ozturk, C.: A novel clustering approach: artificial bee colony (ABC) algorithm. J. Appl. Soft Comput. J. 11(1), 652–657 (2011)
Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firefly algorithm: performance study. J. Swarm Evol. Comput. 1(3), 164–171 (2011)
Xiao, L.: A clustering algorithm based on artificial fish school. In: 2nd International Conference on Computer Engineering and Technology (ICCET), pp. 766–769 (2010)
Hamdi, A., Monmarché, N., Slimane, M., Alimi, A.M.: Fuzzy rules for ant based clustering algorithm. Int. J. Adv. Fuzzy Syst. (2016)
Acknowledgment
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hamdi, A., Monmarché, N., Slimane, M., Alimi, A.M. (2017). Intelligent Hybrid Algorithm for Unsupervised Data Clustering Problem. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-52941-7_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52940-0
Online ISBN: 978-3-319-52941-7
eBook Packages: EngineeringEngineering (R0)