A memetic-GRASP algorithm for clustering

ICEIS 2008 - Proceedings of the 10th International Conference on Enterprise Information Systems

Volumn AIDSS, Issue , 2008, Pages 36-43

  Marinakis, Yannis ^a   Marinaki, Magdalene ^a   Matsatsinis, Nikolaos ^a   Zopounidis, Constantin ^a  

^a TECHNICAL UNIVERSITY OF CRETE   (Greece)

Author keywords

Clustering analysis; Feature selection problem; GRASP; Memetic algorithms; Particle swarm optimization

Indexed keywords

ANT-COLONY OPTIMIZATION; BASIC PRINCIPLES; CLUSTERING ANALYSIS; CLUSTERING PROBLEMS; DATA SETS; FEATURE SELECTION PROBLEM; GRASP; GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURE; K CLUSTER; MEMETIC; MEMETIC ALGORITHMS; META-HEURISTIC METHODS; PHYSICAL MOVEMENTS; PSO ALGORITHMS; TWO PHASE ALGORITHM; UCI MACHINE LEARNING REPOSITORY;

ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS; DECISION THEORY; GENETIC ALGORITHMS; INFORMATION SYSTEMS; LEARNING ALGORITHMS; PARTICLE SWARM OPTIMIZATION (PSO); TABU SEARCH;

CLUSTERING ALGORITHMS;

EID: 55349112531     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (36)

1. Aha, D.W., and Bankert, R.L., 1996. A Comparative Evaluation of Sequential Feature Selection Algorithms. In Artificial Intelligence and Statistics, Fisher, D. and J.-H. Lenx (Eds.). Springer-Verlag, New York.
2. Azzag, H., Venturini, G., Oliver, A., Gu, C, 2007. A Hierarchical Ant Based Clustering Algorithm and its Use in Three Real-World Applications, European Journal of Operational Research, 179, 906-922.
3. Cano, J.R., Córdon, O., Herrera, F., Sánchez, L., 2002. A GRASP Algorithm for Clustering, In IBERAMIA 2002, LNAI 2527, Garijo, F.J., Riquelme, J.C. and M. Toro (Eds.). Springer-Verlag, Berlin Heidelberg, 214-223.
4. Cantu-Paz, E., Newsam, S., Kamath C., 2004. Feature Selection in Scientific Application, In Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 788-793.
5. Chu, S., Roddick, J., 2000. A Clustering Algorithm Using the Tabu Search Approach with Simulated Annealing. In Data Mining II-Proceedings of Second International Conference on Data Mining Methods and Databases, Ebecken, N. and C. Brebbia (Eds.). Cambridge, U.K., 515-523.
6. Dorigo, M, Stiitzle, T., 2004. Ant Colony Optimization. A Bradford Book, The MIT Press, Cambridge, Massachusetts, London, England.
7. Feo, T.A., Resende, M.G.C., 1995. Greedy Randomized Adaptive Search Procedure, Journal of Global Optimization, 6, 109-133.
8. Glover, F., 1989. Tabu Search I, ORSA Journal on Computing, 1 (3), 190-206.
9. Glover, F., 1990. Tabu Search II, ORSA Journal on Computing, 2(1), 4-32.
10. Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, INC. Massachussets.
11. Holland, J.H., 1975. Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.
12. Jain, A., Zongker D., 1997. Feature Selection: Evaluation, Application, and Small Sample Performance, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 153-158.
13. Jain, A.K., Murty, M.N., Flynn, P.J., 1999. Data Clustering: A Review, ACM Computing Surveys, 31 (3), 264-323.
14. Kao, Y., Cheng, K., 2006. An ACO-Based Clustering Algorithm, In ANTS 2006, LNCS 4150, M. Dorigo et al. (Eds.). Springer-Verlag, Berlin Heidelberg, 340-347.
15. Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In Proceedings of 1995 IEEE International Conference on Neural Networks, 4, 1942-1948.
16. Kennedy, J., Eberhart, R, 1997. A discrete binary version of the particle swarm algorithm. In Proceedings of 1997 IEEE International Conference on Systems, Man, and Cybernetics, 5, 4104-4108.
17. Kennedy, J., Eberhart, R., Shi, Y., 2001. Swarm Intelligence. Morgan Kaufmann Publisher, San Francisco.
18. Li, Z., Tan, H.-Z., 2006. A Combinational Clustering Method Based on Artificial Immune System and Support Vector Machine, In KES 2006, Part I, LNAI 4251, Gabrys, B., Howlett, R.J. and L.C. Jain (Eds.). Springer-Verlag Berlin Heidelberg, 153-162.
19. Liao, S.-H., Wen, C.-H., 2007. Artificial Neural Networks Classification and Clustering of Methodologies and Applications - Literature Analysis from 1995 to 2005, Expert Systems with Applications, Vol. 32, pp. 1-11.
20. Liu, Y., Liu, Y., Wang, L., Chen, K., 2005. A Hybrid Tabu Search Based Clustering Algorithm, In KES 2005, LNAI 3682, R. Khosla et al. (Eds.). Springer-Verlag, Berlin Heidelberg, 186-192.
21. Marinakis, Y., Migdalas, A., Pardalos, P.M., 2005. Expanding Neighborhood GRASP for the Traveling Salesman Problem, Computational Optimization and Applications, 32, 231-257.
22. Marinakis Y., Marinaki, M., Doumpos, M., Matsatsinis, N., Zopounidis, C, 2007. Optimization of Nearest Neighbor Classifiers via Metaheuristic Algorithms for Credit Risk Assessment, Journal of Global Optimization, (accepted).
23. Mirkin, B., 1996. Mathematical Classification and Clustering, Kluwer Academic Publishers, Dordrecht, The Netherlands.
24. Moscato, P., Cotta C, 2003. A Gentle Introduction to Memetic Algorithms. In Handbooks of Metaheuristics, Glover, F., and G.A., Kochenberger (Eds.). Kluwer Academic Publishers, Dordrecht, 105-144.
25. Paterlini, S., Krink, T., 2006. Differential Evolution and Particle Swarm Optimisation in Partitional Clustering, Computational Statistics and Data Analysis, 50, 1220-1247.
26. Ray, S., Turi, R.H., 1999. Determination of Number of Clusters in K-means Clustering and Application in Colour Image Segmentation. In Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT99), Calcutta, India.
27. Resende, M.G.C., Ribeiro, C.C., 2003. Greedy Randomized Adaptive Search Procedures. In Handbooks of Metaheuristics, Glover, F., and G.A., Kochenberger (Eds.). Kluwer Academic Publishers, Dordrecht, 219-249.
28. Rokach, L., Maimon, O., 2005. Clustering Methods, In Data Mining and Knowledge Discovery Handbook, Maimon, O. and L. Rokach (Eds.). Springer, New York, 321-352.
29. Shen, J., Chang, S.I., Lee, E.S., Deng, Y., Brown, S.J., 2005. Determination of Cluster Number in Clustering Microarray Data, Applied Mathematics and Computation, 169, 1172-1185.
30. Sheng, W., Liu, X., 2006. A Genetic k-Medoids Clustering Algorithm, Journal of Heuristics, 12, 447-466.
31. Shi, Y., Eberhart, R. 1998. A modified particle swarm optimizer. In Proceedings of 1998 IEEE World Congress on Computational Intelligence, 69-73.
32. Sun, J., Xu, W., Ye, B., 2006. Quantum-Behaved Particle Swarm Optimization Clustering Algorithm, In ADMA 2006, LNAI 4093, Li, X., Zaiane, O.R. and Z. Li (Eds.). Springer-Verlag, Berlin Heidelberg, 340-347.
33. Xu, R., Wunsch II, D., 2005. Survey of Clustering Algorithms, IEEE Transactions on Neural Networks, 16(3), 645-678.
34. Yang, Y., Kamel, M.S., 2006. An Aggregated Clustering Approach Using Multi-Ant Colonies Algorithms, Pattern Recognition, 39, 1278-1289.
35. Yeh, J.-Y., Fu, J.C., 2007. A Hierarchical Genetic Algorithm for Segmentation of Multi-Spectral Human-Brain MRI, Expert Systems with Applications, doi:10.1016/j.eswa.2006.12.012.
36. Younsi, R., Wang, W., 2004. A New Artificial Immune System Algorithm for Clustering, In IDEAL 2004, LNCS 3177, Z.R. Yang et al. (Eds.). Springer-Verlag, Berlin Heidelberg, 58-64.