Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
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ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
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IEEE Transactions on Knowledge and Data Engineering
Generation of pairwise test sets using a simulated bee colony algorithm
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Engineering optimizations via nature-inspired virtual bee algorithms
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Cooperative bees swarm for solving the maximum weighted satisfiability problem
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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This study investigates the use of a biologically inspired meta-heuristic algorithm to extract rule sets from clustered categorical data. A computer program which implemented the algorithm was executed against six benchmark data sets and successfully discovered the underlying generation rules in all cases. Compared to existing approaches, the simulated bee colony (SBC) algorithm used in this study has the advantage of allowing full customization of the characteristics of the extracted rule set, and allowing arbitrarily large data sets to be analyzed. The primary disadvantages of the SBC algorithm for rule set extraction are that the approach requires a relatively large number of input parameters, and that the approach does not guarantee convergence to an optimal solution. The results demonstrate that an SBC algorithm for rule set extraction of clustered categorical data is feasible, and suggest that the approach may have the ability to outperform existing algorithms in certain scenarios.