A High Efficient Association Rule Mining Algorithm based on Intelligent Computation

Fengxiang Wu

Abstract


Abstract—Data mining is to use automated data analysis techniquesto uncover previously undetected relationships among data items. In datamining, association rule mining is a prevalent and well researched method for discovering useful relations between variables inlarge databases. In this paper, we investigate the principle of Apriori, direct hash and pruning and alsostudy the drawback of them. The first is constructing hash table withoutconfliction is theoretically optimal, but it needs consume a lot of memoryspace and space utilization is low. The second is that it does not have hashtree data structure leading to too long insert and search  time. So we propose a new association rule mining algorithm based on differential evolutionarycomputation. Theexperiment results show that our proposed algorithm has better execution timeand accuracy, which can be used in electroniccommerce system.

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4810


Keywords


Index Terms—Apriori, association rule, direct hash and prunning, differential evolutionary computation

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