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An Improved Model for Ant based Clustering

by Saroj Bala, S.i. Ahson, R.p. Agarwal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 59 - Number 20
Year of Publication: 2012
Authors: Saroj Bala, S.i. Ahson, R.p. Agarwal
10.5120/9817-4370

Saroj Bala, S.i. Ahson, R.p. Agarwal . An Improved Model for Ant based Clustering. International Journal of Computer Applications. 59, 20 ( December 2012), 9-12. DOI=10.5120/9817-4370

@article{ 10.5120/9817-4370,
author = { Saroj Bala, S.i. Ahson, R.p. Agarwal },
title = { An Improved Model for Ant based Clustering },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 59 },
number = { 20 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 9-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume59/number20/9817-4370/ },
doi = { 10.5120/9817-4370 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:04:46.590545+05:30
%A Saroj Bala
%A S.i. Ahson
%A R.p. Agarwal
%T An Improved Model for Ant based Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 59
%N 20
%P 9-12
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Grouping different objects possessing inherent similarities in clusters has been addressed as the clustering problem among researchers. The development of new metaheuristics has given another direction to data clustering research. Swarm intelligence technique using ant colony optimization provides clustering solutions based on brood sorting. After basic ant model of clustering, number of improvements has been proposed. But the ant clustering still suffers with low convergence. This paper presents a novel model of intelligent movement of ants including the negative pheromone and direction selection. Negative pheromone plays a role of barrier in the direction of empty area and direction selection avoids the calculations not contributing to the clustering process. Simulations have shown good results.

References
  1. Deneubourg, J. L. , Goss, S. , Franks, N. , Franks, A. , Detrain C. and Chretien, L. 1991. The dynamics of collective sorting: Robot-like ants and ant-like robots. Simulation of Adaptative Behavior: From Animals to Animats, pp. 356-363.
  2. Dorigo, M. and Socha, K. 2006. An Introduction to Ant Colony Optimization. Technical Report No. TR/IRIDIA/2006-010. Universite Libre de Bruxelles, Belgium.
  3. Dorigo, M. , Maniezzo, V. and Colorni, A. 1996. Ant System: Optimization by a colony of cooperating agents. IEEE Trans. Syst. , Man, and Cybern. Part B, 26(1), 29.
  4. Dorigo, M. and Gambardella. L. M. 1997. Ant Colony System: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. , 1(1), 53.
  5. Gutowitz, H. 1993. Complexity-seeking ants. Proceedings of the Third European Conference on Artificial Life.
  6. Jiang, H. , Yu Q. and Yu G. 2010. An Improved Ant Colony Clustering Algorithm. 3rd International Conference on Biomedical Engineering and Informatics published in IEEE 978-1-4244-6498-2/10. pp 2368-2372.
  7. Jie S. , Ying L. and Zhimin, C. 2006. Incremental Web Usage Mining Based on Active Ant Colony Clustering. Wuhan Univ. Journal of Natural Sciences. pp. 1081-1085, Vol 11, No. 5.
  8. Li, S. , Huang, W. and Tan, Y. 2010. An Improved Ant-Colony Clustering Algorithm Based on the Innovational Distance Calculation Formula. Third International Conference on Knowledge Discovery and Data Mining published in IEEE 978-0-7695-3923-2/10. pp. 342-346.
  9. Li, L. , Wu W. and Rong Q. 2010. Research on Hybrid Clustering Based on Density and Ant Colony Algorithm. Second International Workshop on Education Technology and Computer Science published in IEEE 978-0-7695-3987-4/10, pp. 222-225.
  10. Lumer, E. D. and Faieta, B. 1994. Diversity and adaptation in populations of clustering ants. Proceedings of the Third International Conference on the Simulation of Adaptative Behavior: From Animals to Animats 3, pp. 449-508. MIT Press.
Index Terms

Computer Science
Information Sciences

Keywords

clustering ant colony optimization pheromone

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