Clustering using Cellular Genetic Algorithms

Nuno Leite, Fernando Melício, Agostinho Rosa

2015

Abstract

The goal of the clustering process is to find groups of similar patterns in multidimensional data. In this work, the clustering problem is approached using cellular genetic algorithms. The population structure adopted in the cellular genetic algorithm contributes to the population genetic diversity preventing the premature convergence to local optima. The performance of the proposed algorithm is evaluated on 13 test databases. An extension to the basic algorithm was also investigated to handle instances containing non-linearly separable data. The algorithm is compared with nine non-evolutionary classification techniques from the literature, and also compared with three nature inspired methodologies, namely Particle Swarm Optimization, Artificial Bee Colony, and the Firefly Algorithm. The cellular genetic algorithm attains the best result on a test database. A statistical ranking of the compared methods was made, and the proposed algorithm is ranked fifth overall.

References

  1. Alba, E. and Dorronsoro, B. (2008). Cellular Genetic Algorithms. Springer Publishing Company, Incorporated, 1st edition.
  2. Borgelt, C. (2006). Prototype-based classification and clustering. PhD thesis, Otto-von-Guericke-Universität Magdeburg, Universitätsbibliothek.
  3. Cahon, S., Melab, N., and Talbi, E.-G. (2004). Paradiseo: A framework for the reusable design of parallel and distributed metaheuristics. Journal of Heuristics, 10(3):357-380.
  4. Duda, R. O., Hart, P. E., and Stork, D. G. (2000). Pattern Classification (2nd Edition). Wiley-Interscience.
  5. Falco, I. D., Cioppa, A. D., and Tarantino, E. (2007). Facing classification problems with particle swarm optimization. Appl. Soft Comput., 7(3):652-658.
  6. Falkenauer, E. (1998). Genetic Algorithms and Grouping Problems. John Wiley & Sons, Inc., New York, NY, USA.
  7. García, S. and Herrera, F. (2008). An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. Journal of Machine Learning Research, 9:2677-2694.
  8. Jain, A. K. (2010). Data clustering: 50 years beyond kmeans. Pattern Recognition Letters, 31(8):651 - 666. Award winning papers from the 19th International Conference on Pattern Recognition (ICPR)19th International Conference in Pattern Recognition (ICPR).
  9. Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: A review. ACM Comput. Surv., 31(3):264- 323.
  10. Karaboga, D. and Ozturk, C. (2011). A novel clustering approach: Artificial bee colony (abc) algorithm. Appl. Soft Comput., 11(1):652-657.
  11. Lichman, M. (2013). UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences.
  12. Lourenc¸o, A., Bul ò, S. R., Rebagliati, N., Fred, A. L. N., Figueiredo, M. A. T., and Pelillo, M. (2015). Probabilistic consensus clustering using evidence accumulation. Machine Learning, 98(1-2):331-357.
  13. Marinakis, Y., Marinaki, M., Doumpos, M., Matsatsinis, N., and Zopounidis, C. (2008). A hybrid stochastic geneticgrasp algorithm for clustering analysis. Operational Research, 8(1):33-46.
  14. Maulik, U., Bandyopadhyay, S., and B, S. (2000). Genetic algorithm-based clustering technique. Pattern Recognition, 33:1455-1465.
  15. Mirkin, B. (1996). Mathematical Classification and Clustering. Kluwer Academic Publishers, Dordrecht, The Netherlands.
  16. Ni, J., Li, L., Qiao, F., and Wu, Q. (2013). A novel memetic algorithm and its application to data clustering. Memetic Computing, 5(1):65-78.
  17. Niknam, T. and Amiri, B. (2010). An efficient hybrid approach based on pso, aco and k-means for cluster analysis. Appl. Soft Comput., 10(1):183-197.
  18. Selim, S. Z. and Ismail, M. A. (1984). K-means-type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Trans. Pattern Anal. Mach. Intell., 6(1):81-87.
  19. Senthilnath, J., Omkar, S., and Mani, V. (2011). Clustering using firefly algorithm: Performance study. Swarm and Evolutionary Computation, 1(3):164 - 171.
  20. Zhao, J., Lei, X., Wu, Z., and Tan, Y. (2014). Clustering using improved cuckoo search algorithm. In Tan, Y., Shi, Y., and Coello, C., editors, Advances in Swarm Intelligence, volume 8794 of Lecture Notes in Computer Science, pages 479-488. Springer International Publishing.
Download


Paper Citation


in Harvard Style

Leite N., Melício F. and Rosa A. (2015). Clustering using Cellular Genetic Algorithms . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 366-373. DOI: 10.5220/0005647403660373


in Bibtex Style

@conference{ecta15,
author={Nuno Leite and Fernando Melício and Agostinho Rosa},
title={Clustering using Cellular Genetic Algorithms},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={366-373},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005647403660373},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Clustering using Cellular Genetic Algorithms
SN - 978-989-758-157-1
AU - Leite N.
AU - Melício F.
AU - Rosa A.
PY - 2015
SP - 366
EP - 373
DO - 10.5220/0005647403660373