Novel approach to optimize quantitative association rules by employing multi-objective genetic algorithm

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

Volumn 3533 LNAI, Issue , 2005, Pages 560-562

  Kaya, Mehmet ^a   Alhajj, Reda ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; GENETIC ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION;

LETTER RECOGNITION DATABASE; MULTI-OBJECTIVE GENETIC ALGORITHM; UCI MACHINE LEARNING REPOSITORY;

ARTIFICIAL INTELLIGENCE;

EID: 26944451630     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11504894_78     Document Type: Conference Paper

References (4)

1. M. Kaya, R. Alhajj, F. Polat and A. Arslan, "Efficient Automated Mining of Fuzzy Association Rules," Proc. of the International Conference on Database and Expert Systems with Applications, 2002.
2. M. Kaya and R. Alhajj, "Facilitating Fuzzy Association Rules Mining by Using Multi-Objective Genetic Algorithms for Automated Clustering," Proc. of IEEE ICDM, Melbourne, FL, 2003.
3. 9 T. Fukuda, Y. Morimoto, S. Morishita and T. Tokuyama, "Mining Optimized Association Rules for Numeric Attributes," Proc. of ACM SIGACT-SIGMOD-SIGART PODS, 1996.
4. 12 R. Rastogi and K. Shim, "Mining Optimized Association Rules with Categorical and Numeric Attributes," IEEE TKDE, Vol.14, No.1, pp.29-50, 2002.