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Reseach Article

Agglomerative Ants for Data Clustering

by Saroj Bala, S. I. Ahson, R. P. Agarwal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 21
Year of Publication: 2012
Authors: Saroj Bala, S. I. Ahson, R. P. Agarwal
10.5120/7469-0113

Saroj Bala, S. I. Ahson, R. P. Agarwal . Agglomerative Ants for Data Clustering. International Journal of Computer Applications. 47, 21 ( June 2012), 1-4. DOI=10.5120/7469-0113

@article{ 10.5120/7469-0113,
author = { Saroj Bala, S. I. Ahson, R. P. Agarwal },
title = { Agglomerative Ants for Data Clustering },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 21 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number21/7469-0113/ },
doi = { 10.5120/7469-0113 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:42:25.201685+05:30
%A Saroj Bala
%A S. I. Ahson
%A R. P. Agarwal
%T Agglomerative Ants for Data Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 21
%P 1-4
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering is a data mining technique for the analysis of data in various areas such as pattern recognition, image processing, information science, bioinformatics etc. Hierarchical clustering techniques form the clusters based on top-down and bottom-up approaches. Hierarchical agglomerative clustering is a bottom-up clustering method. Ant based clustering methods form clusters by picking and dropping the objects according to surroundings. This paper proposes an agglomerative clustering algorithm, AGG_ANTS based on ant colonies. AGG_ANTS clusters the objects by moving ants on the grid and merging their loads according to similarity resulting in bigger clusters. It avoids the calculation of similarity in the surrounding and pick/drop of objects again and again resulting in a more efficient algorithm.

References
  1. Marco Dorigo, V. Maniezzo and A. Colorni, 'Ant System: Optimization by a colony of cooperating agents', IEEE Transactions on Systems, Man, and Cybernetics-Part B, Vol. 26, No. 1, pp. 1-13, 1996.
  2. J. L. Deneubourg, S. Gross, N. R. Franks, A. Sendova-Franks, C. Detrain and L. Chretien, 'The dynamics of collective sorting: Robot-like ants and ant-like robots', Simulation of Adaptative Behavior: From Animals to Animats, pp. 356-363, 1991.
  3. E. D. Lumer and B. Faieta, 'Diversity and adaptation in populations of clustering ants', Proc. of the Third International Conference on The Simulation of Adaptative Behavior: From Animals to Animats 3, pp. 449-508. MIT Press, 1994.
  4. H. Gutowitz, 'Complexity-seeking ants', In Proc. of the Third European Conference on Artificial Life, 1993.
  5. N. Monmarche, M. Slimane and G. Venturini, 'On improving clustering in numerical databases with artificial ants', Advances in Artificial Life, pp. 626-635, 1999.
  6. W. Ngenkaew, Satoshi Ono and S. Nakayama, 'Pheromone- Based Concept in Ant Clustering', In Proc. of 3rd International conf. on Intelligent System and Knowledge Engineering, pp. 308-312, 2008.
  7. Hong Jiang, Qingsong Yu and Yu Gong, 'An Improved Ant Colony Clustering Algorithm', 3rd International Conference on Biomedical Engineering and Informatics, IEEE 978-1-4244-6498-2/10, pp. 2368-2372, 2010.
  8. A. K. Jain, M. N. Murty and P. J. Flynn. 'Data Clustering: A Review', ACM Computing Surveys, Vol. 31,No. 3, pp. 264-323, September 1999.
  9. J. Handl, J. Knowles and M. Dorigo, ' Ant Based clustering: a comparative study of its relative performance with respect to k-means, average link and 1-D-som', Technical Report No. TR/IRIDIA/2003-24, Universite Libre de Bruxelles, Belgium, 2003.
  10. J. B. Brown and M. Huber, 'Pseudo-hierarchical ant-based clustering using Automatic Boundary Formation and a Heterogeneous Agent Hierarchy to Improve Ant-Based Clustering Performance', 2010 IEEE international conference on SMC, pp. 2016-2024, 2010.
  11. Shanfei Li, Wei Huang, Kewei Yang, Yuejin Tan, ' An Improved Ant-Colony Clustering Algorithm Based On the Innovational Distance Calculation Formula', 2010 Third International Conference on Knowledge Discovery and Data Mining, pp. 342-346, 2010.
  12. I. El-Feghi, M. Errateeb, M. Ahmadi and M. A. Sid-Ahmed, 'An Adaptive Ant-Based Clustering Algorithm with Improved Environment Perception' International Conference on Systems, Man, and Cybernetics, San Antonio, TX, USA - October 2009 published in IEEE 978-1-4244-2794-9/09, pp. 1431-1438, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering Hierarchical Agglomerative Ant Colony