AN EFFICIENT HYBRID METHOD FOR CLUSTERING PROBLEMS

H. Panahi, R. Tavakkoli-Moghaddam

Abstract

This paper presents a hybrid efficient method based on ant colony optimization (ACO) and genetic algorithms (GA) for clustering problems. This proposed method assumes that agents of ACO has life cycle which is variable and changes by a special function. We also apply three local searches on the basis of heuristic rules for the given clustering problem. This proposed method is implemented and tested on two real datasets. Further, its performance is compared with other well-known meta-heuristics, such as ACO, GA, simulated annealing (SA), and tabu search (TS). At last, paired comparison t-test is also applied to proof the efficiency of our proposed method. The associated output gives very encouraging results; however, the proposed method needs longer time to proceed.

References

  1. Al-Sultan, K.S., 1995. A Tabu search approach to the clustering problem, Pattern Recogn. 28 (9) 1443-1451.
  2. Banfield, J., Raftery, A., 1993. Model-based Gaussian and non- Gaussian clustering, Biometrics, 49, 803-821.
  3. Dorigo, M., Maniezzo, V. Colorni, A., 1996. The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26 (1) 29-41.
  4. Jiang, J.-H., Wang, J.H., Chu, X., Yu, R.-Q., 1997. Clustering data using a modified integer genetic algorithm (IGA). Analytica Chimica Acta 354, 263- 274.
  5. Lin, H-J., Yang, F-W., Kao, Y-T., 2005. An efficient GAbased clustering technique, Tamkang Journal of Science and Engineering 8 (2) 113-122.
  6. Murthy, C.A., Chowdhury, N., 1996. In search of optimal clusters using genetic algorithms. Pattern Recognition Letters. 17 (8) 825- 832.
  7. Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J. 1998. UCI Repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA, http://www.ics.uci.edu/mlearn/MLRepository.html.
  8. Paterlini, S., Krink, T., 2006. Differential evolution and particle swarm optimisation in partitional clustering, Computational Statistics and Data Analysis 50 (5) 1220-1247.
  9. Selim, S.Z., Al-Sultan, K.S., 1991. A simulated annealing algorithm for the clustering problem. Pattern Recognition 24 (10) 1003-1008.
  10. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D., 2004. An ant colony approach for clustering, Analytica Chimica Acta 509, 187-195.
  11. Sun, L.-X., Xie, Y.-L., Song, X.-H., Wang, J.-H. Yu, R.- Q., 1994. Cluster analysis by simulated annealing. Computers & Chemistry 18 (2) 103-108.
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Paper Citation


in Harvard Style

Panahi H. and Tavakkoli-Moghaddam R. (2008). AN EFFICIENT HYBRID METHOD FOR CLUSTERING PROBLEMS . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 288-294. DOI: 10.5220/0001703002880294


in Bibtex Style

@conference{iceis08,
author={H. Panahi and R. Tavakkoli-Moghaddam},
title={AN EFFICIENT HYBRID METHOD FOR CLUSTERING PROBLEMS},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2008},
pages={288-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001703002880294},
isbn={978-989-8111-37-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - AN EFFICIENT HYBRID METHOD FOR CLUSTERING PROBLEMS
SN - 978-989-8111-37-1
AU - Panahi H.
AU - Tavakkoli-Moghaddam R.
PY - 2008
SP - 288
EP - 294
DO - 10.5220/0001703002880294