Combining advantages of new chromosome representation scheme and multi-objective genetic algorithms for better clustering

Intelligent Data Analysis

Volumn 10, Issue 2, 2006, Pages 163-182

  Korkmaz, Emin Erkan ^a   Du, Jun ^b   Alhajj, Reda ^b,c   Barker, Ken ^b  

^a YEDITEPE UNIVERSITY   (Turkey)

^b UNIVERSITY OF CALGARY   (Canada)

^c GLOBAL UNIVERSITY   (Lebanon)

Author keywords

Clustering; genetic algorithms; k means; linkage encoding; multi objective

Indexed keywords

CHROMOSOMES; ENCODING (SYMBOLS); GENETIC ALGORITHMS; PARETO PRINCIPLE; SIGNAL ENCODING;

CLUSTERING; K-MEANS; LINKAGE-ENCODING; MINIMIZING THE NUMBER OF; MULTI OBJECTIVE; MULTI-OBJECTIVE GENETIC ALGORITHM; REPRESENTATION SCHEMES; WITHIN-CLUSTER VARIATION;

K-MEANS CLUSTERING;

EID: 63149100088     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2006-10205     Document Type: Article

References (32)

1. A. Pen, J. Lozano and J. Larran, An empirical comparison of four initialization methods for the k-means algorithm, Pattern Recognition Letters 20(10) (1999), 1027-1040.
2. G.P. Babu and M.N. Murty, A near-optimal initial seed value selection in k-means algorithm using a genetic algorithm, Pattern Recognition Letters 14(10) (1993), 763-769.
3. P. Berkhin, Survey of clustering data mining techniques, Technical report, Accrue Software, San Jose, CA, 2002.
4. J. Bezdek and S. Boggavarapu, Genetic algorithm guided clustering, Proc. 1st IEEE Conf. on Evolutionary Computation, 1994, 34-39.
5. C.L. Blake and C.J. Merz, UCI repository of machine learning databases, 2000.
6. A. Bremner, Reviews: The Book of Numbers, by John Horton Conway and Richard K. Guy, American Mathematical Monthly 104(9) (November 1997), 884-.
7. D.S. Burke, K.A. De Jong and J.J. Grefenstette, Connie Loggia Ramsey, and Annie S. Wu. Putting more genetics into genetic algorithms, Evolutionary Computation 6(4) (Winter 1998), 387-410.
8. Rowena Marie Cole, Clustering with genetic algorithms, Master's thesis, Nedlands 6907, Australia, 1998.
9. I. Dhillon, J. Fan and Y. Guan, Efficient clustering of very large document collections, in: Data Mining for Scientific and Engineering Applications, R. Grossman, C. Kamath and R. Naburu, eds, Kluwer Academic Publishers, 2001.
10. E. Falkenauer, Genetic Algorithms and Grouping Problems, John Wiley & Sons, 1998.
11. E. Falkenauer, A new representation and operators for genetic algorithms applied to grouping problems, Evolutionary Computation 2(2) (1994), 123-144.
12. D. Gibson, J.M. Kleinberg and P. Raghavan, Clustering categorical data: An approach based on dynamical systems, VLDB Journal: Very Large Data Bases 8(3-4) (February 2000), 222-236. Electronic edition.
13. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Mass., 1989. An introductory textbook and guide to current research. Contains examples, exercises, and sample programs as well as proofs of most of the major GA theorems on crossover and implicit parallelism.
14. S. Guha, R. Rastogi and K. Shim, CURE: an efficient clustering algorithm for large databases, In Laura Haas and Ashutosh Tiwary, editors, Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data: June 1-4, 1998, Seattle, Washington, USA, Volume 27(2) of SIGMOD Record (ACM Special Interest Group on Management of Data), 73-84, New York, NY 10036, USA, 1998. ACM Press.
15. L.O. Hall, I.B. Ö zyurt and J.C. Bezdek, Clustering with a genetically optimized approach, IEEE Trans. on Evolutionary Computation 3(2) (1999), 103-112.
16. J. Han and M. Kamer, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001.
17. J. Hartigan, Clustering Algorithms, Wiley, 1975.
18. J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975.
19. J. Horn, N. Nafpliotis and D.E. Goldberg, A Niched Pareto Genetic Algorithm for Multiobjective Optimization, (Vol. 1), In Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Piscataway, New Jersey, 1994. IEEE Service Center, 82-87.
20. A.K. Jain and R.C. Dubes, Algorithms for Clustering Data, Prentice Hall International, 1988.
21. D.A. Jones and M.A. Beltramo. Solving partitioning problems with genetic algorithms, in: Proceedings of the 4th International Conference on Genetic Algorithms, L.B. Belew and R.K. Booker, eds, San Diego, CA, July 1991. Morgan Kaufmann, 442-449.
22. L. Kaufman and P.J. Rousseeuw, Finding Groups in Data, John Wiley & Sons, 1990.
23. K. Krishna and M. Murty, Genetic k-means algorithm, IEEE Transactions on Systems, Man, and Cybernetics-PartB: Cybernetics 29(3) (1999), 433-439.
24. U. Maulik and S. Bandyopadhyay, Genetic algorithm-based clustering technique, Pattern Recognition 33 (2000), 1455-1465.
25. G.J. McLachlan and K.E. Basford, Chapters 1 and 2, In McLachlan and Basford, editors, Mixture models: inference and applications to clustering, Marcel Dekker, Inc., 1988, 1-69.
26. R. Ng and J. Han, Efficient and effective clustering method for spatial data mining, In Proc. of 1994 Int'l Conf. on Very Large Data Bases (VLDB'94), September 1994, 144-155.
27. N.J. Radcliffe, Forma analysis and random respectful recombination, in: Proceedings of the 4th International Conference on Genetic Algorithms, L.B. Belew and R.K. Booker, eds, San Diego, CA, July 1991. Morgan Kaufmann, 222-229.
28. B. Rekiek, P. de Lit, F. Pellichero, T. L'Eglise, P. Fouda, E. Falkenauer and A. Delchambre, A multiple objective grouping genetic algorithm for assembly line design, Journal of Intelligent Manufacturing 12(5-6) (2001), 467-485.
29. J.T. Richardson, M.R. Palmer, G. Liepina and M. Hilliard, Some guidlines for genetic algorithms with penalty functions, In Proc. of the 3rd Int. Conf. on Genetic Algorithms, Morgan Kaufman, 1989.
30. E.H. Ruspini, Numerical methods for fuzzy clustering, Inform. Sci. 2(3) (1970), 19-150.
31. I. Sarafis, A.M.S. Zalzala and P. Trinder, A genetic rule-based data clustering toolkit, in: Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, D.B. Fogel, M.A. El-Sharkawi, X. Yao, G. Greenwood, H. Iba, P. Marrow and M. Shackleton, eds, IEEE Press, 2002, pp. 1238-1243.
32. E. Schikuta and M. Erhart, BANG-Clustering: A novel grid-clustering algorithm for huge data sets, Lecture Notes in Computer Science 1451 (1998), 867.