Mining association rules between sets of items in large databases
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Fast Algorithms for Mining Association Rules in Large Databases
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IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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This paper deals with Association Rules Mining algorithms for very large databases and especially for those existing on the web. The numerous polynomial exact algorithms already proposed in literature treated somehow in an efficient way data sets with reasonable size. However they are not capable to cope with a huge amount of data in the web context where the respond time must be very short. This paper, mainly proposes two new Association Rules Mining algorithms based on Genetic metaheuristic and Bees Swarm Optimization respectively. Experimental results show that concerning both the fitness criterion and the CPU time, IARMGA algorithm improved AGA and ARMGA two other versions based on genetic algorithm already proposed in the literature. Moreover, the same experience shows that concerning the fitness criterion, BSO-ARM achieved slightly better than all the genetic approaches. On the other hand, BSO-ARM is more time consuming. In all cases, we observed that the developed approaches yield useful association rules in a short time when comparing them with previous works.