Linkage discovery through data mining: Research frontier

IEEE Computational Intelligence Magazine

Volumn 5, Issue 1, 2010, Pages 10-13

  Ting, Chuan Kang ^a   Zeng, Wei Ming ^a   Lin, Tzu Chieh ^a  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CANDIDATE SOLUTION; DATA MINING METHODS; GLOBALOPTIMUM; MUTATION OPERATIONS; NATURAL EVOLUTION; OPTIMIZATION PROBLEMS; RESEARCH FRONTIERS; SALIENT FEATURES;

ASSOCIATIVE PROCESSING; GASES; MINING;

DATA MINING;

EID: 76349105090     PISSN: 1556603X     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCI.2009.935310     Document Type: Article

References (18)

1. J. Han and M. Kamber, Data Mining: Concepts and Techniques. San Mateo, CA: Morgan Kaufmann, 2000.
2. A. A. Freitas, Data Mining and Knowledge Discovery with Evolutionary Algorithms. Berlin: Springer-Verlag, 2002.
3. S. Mitra, S. K. Pal, and P. Mitra, "Data mining in soft computing framework: A survey," IEEE Trans. Neural Netw., vol.13, no.1, pp. 3-14, 2002.
4. J. Holland, Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, 1975.
5. D. E. Goldberg, The Design of Innovation: Lessons From and For Competent Genetic Algorithms. Berlin: Springer- Verlag, 2002.
6. M. Pelikan, D. E. Goldberg, and E. Cantú-Paz, "BOA: The Bayesian optimization algorithm," in Proc. 1999 Genetic and Evolutionary Computation Conf., 1999, pp. 525-532.
7. H. Mühlenbein and T. Mahnig, "FDA - A scalable evolutionary algorithm for the optimization for the optimization of additively decomposed functions," Evolution. Computat., vol.7, no.4, pp. 353-376, 1999.
8. G. R. Harik, "Linkage learning via probabilistic modeling in the ECGA," Univ. of Illinois at Urbana- Champaign, Illinois Genetic Algorithms Laboratory, Tech. Rep., 1999.
9. M. Munetomo and D. E. Goldberg, "Designing a genetic algorithm using the linkage identification by nonlinearity check," Univ. of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Tech. Rep., 1998.
10. M. Munetomo and D. E. Goldberg, "Identifying linkage by nonlinearity check," Univ. of Illinois at Urbana- Champaign, Illinois Genetic Algorithms Laboratory, Tech. Rep., 1998.
11. M. Munetomo and D. E. Goldberg, "Linkage identification by non-monotonicity detection for overlapping functions," Evolution. Computat., vol.7, no.4, pp. 377-398, 1999.
12. M. Munetomo, "Linkage identification based on epistasis measures to realize efficient genetic algorithms," in Proc. 2002 IEEE Congress on Evolutionary Computation, 2002, pp. 1332-1337.
13. M. Tsuji, M. Munetomo, and K. Akama, "Modeling dependencies of loci with string classification according to fitness differences," in Proc. 2004 Genetic and Evolutionary Computation Conf., 2004, pp. 246-257.
14. M. Tsuji and M. Munetomo, "Linkage analysis in genetic algorithms," in Computational Intelligence Paradigms. Berlin: Springer-Verlag, 2008, pp. 251-279.
15. Y. P. Chen, T. L. Yu, K. Sastry, and D. E. Goldberg, "A survey of genetic linkage learning techniques," Univ. of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Tech. Rep., 2007.
16. M. Tsuji, M. Munetomo, and K. Akama, "A crossover for complex building blocks overlapping," in Proc. 2006 Genetic and Evolutionary Computation Conf., 2006, pp. 1337-1344.
17. R. Agrawal, T. Imielínski, and A. Swami, "Mining association rules between sets of items in large databases," in Proc. 1993 ACM SIGMOD Int. Conf. Management of Data, 1993, vol. 22, no. 2, pp. 207-216.
18. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. 20th Int. Conf. Very Large Data Bases, 1994, vol.1215, pp. 487-499.