Novel clustering approach that employs genetic algorithm with new representation scheme and multiple objectives

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3181, Issue , 2004, Pages 219-228

  Du, Jun ^a   Korkmaz, Erkan ^a   Alhajj, Reda ^a   Barker, Ken ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA WAREHOUSES; ENCODING (SYMBOLS); PARETO PRINCIPLE;

CLASSICAL APPROACH; CLUSTERING PROBLEMS; MINIMIZING THE NUMBER OF; MULTIPLE-OBJECTIVES; OPTIMAL PARTITIONS; PARETO-OPTIMAL SETS; REPRESENTATION SCHEMES; WITHIN-CLUSTER VARIATION;

GENETIC ALGORITHMS;

EID: 33750239051     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30076-2_22     Document Type: Article

References (14)

1. P. Berkhin. Survey of clustering data mining techniques. Technical report, Accrue Software, San Jose, CA, 2002.
2. C.L. Blake and C.J. Merz. UCI repository of machine learning databases, 2000.
3. D.S. Burke and et al. Putting more genetics into genetic algorithms. Evolutionary Computation, 6(4):387-410, 1998.
4. E. Falkenauer. A new representation and operators for genetic algorithms applied to grouping problems. Evolutionary Computation, 2(2):123-144, 1994.
5. E. Falkenauer. Genetic Algorithms and Grouping Problems. John Wiley&Sons, 1998.
6. S. Guha, R. Rastogi, and K. Shim. CURE: an efficient clustering algorithm for large databases. In Proc. of ACM SIGMOD, pages 73-84, New York, 1998.
7. J. Han and M. Kamer. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001.
8. J. Horn, N. Nafpliotis, and D.E. Goldberg. A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In Proc. of IEEE CEC, volume 1, pages 82-87, 1994.
9. D.A. Jones and M.A. Beltramo. Solving partitioning problems with genetic algorithms. In Proc. of the International Conference on Genetic Algorithms, pages 442-449, San Diego, CA, July 1991.
10. K. Krishna and M. Murty. Genetic k-means algorithm. IEEE TSMC-B, 29(3):433-439, 1999.
11. U. Maulik and S. Bandyopadhyay. Genetic algorithm-based clustering technique. Pattern Recognition, 33:1455-1465, 2000.
12. A. Pen, J. Lozano, and J. Larran. An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Letters, 20(10):1027-1040, 1999.
13. E.H. Ruspini. Numerical methods for fuzzy clustering. Inform. Sci., 2(3):19-150, 1970.
14. I. Sarafis, A.M.S. Zalzala, and P. Trinder. A genetic rule-based data clustering toolkit. In Proc. of the Evolutionary Computation Congress, pages 1238-1243, 2002.