Abstract
In this paper, we propose a hybridization between an ant-based clustering algorithm: CAC (Communicating Ants for Clustering) algorithm [5] and a clustering aggregation algorithm: the Furthest algorithm [6]. The CAC algorithm takes inspiration from the sound communication properties of real ants. In this algorithm, artificial ants communicate directly with each other in order to achieve the clustering task. The Furthest algorithm takes as inputs m clusterings given by m different runs of the CAC algorithm, and tries to find a clustering that matches, as possible, all the clusterings given as inputs. This hybridization shows an improvement of the obtained results.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Blake, C., Merz, C.: UCI Repository of machine learning databases, University of California, Irvine, Dept. of Information and Computer Sciences
Cui, X., Potok, T.E., Palathingal, P.: Document Clustering using Particle Swarm Optimization. In: IEEE Swarm Intelligence Symposium, The Westin (2005)
Delgado, M., Skrmeta, A.G., Barber, H.M.: A Tabu Search Approach To The Fuzzy Clustering Problem. In: Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, pp. 125–130 (1997)
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 robots. In: Meyer, J.-J., Wilson, S. (eds.) Proceedings of the First International Conference on Simulation of Adaptative Behavior, Paris, France, pp. 356–365 (1990)
Elkamel, A., Gzara, M., Jamoussi, S., Ben Abdallah, H.: An ant-based algorithm for clustering. In: The 7th ACS/IEEE International Conference on Computer Systems and Applications, Rabat, Morocco, pp. 76-82 (2009)
Gionis, A., Mannila, H., Tsaparas, P.: Clustering Aggregation. ACM Transactions on Knowledge Discovery from Data 1(1), Article 4 (2007)
Guojun, G., Chaoqun, M., Jianhong, W.: Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM Series on Statistics and Applied Probability (2007)
Hickling, R., Brown, R.L.: Analysis of acoustic communication by ants. J. Acoust. Soc. Am. 108(4), 1920–1929 (2000)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)
Knight, T., Timmis, J.: A Multi-Layered Immune Inspired Approach to Data Mining. In: Lotfi, A., Garibaldi, J., John, R. (eds.) Proceedings of the 4th International Conference on Recent Advances in Soft Computing, Nottingham, UK, pp. 266–271 (December 2002)
Lumer, E.D., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Proceedings of the Third International Conference on Simulation of Adaptive Behaviour, pp. 501–508 (1994)
Raghavan, V.V., Birchard, K.: A clustering strategy based on a formalism of the reproductive process in natural systems. In: Proceedings of the Second International Conference on Information Storage and Retrieval, pp. 10–22. ACM, New York (1979)
Selim, S.Z., Alsultan, K.: A simulated annealing algorithm for the clustering problem. Pattern Recognition 24(7), 1003–1008 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Elkamel, A., Gzara, M., Ben-Abdallah, H. (2011). Clustering Aggregation for Improving Ant Based Clustering. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-21515-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21514-8
Online ISBN: 978-3-642-21515-5
eBook Packages: Computer ScienceComputer Science (R0)