Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach

Roni La’biran
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Abstract

The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets.
基于蚁群算法的关联规则优化
Apriori算法使用关联规则挖掘和Apriori算法在数据库中的项之间创建所有可能的关联规则。利用蚁群算法,提出了一种改进关联规则挖掘结果的新算法。以蚁群行为为出发点,提出了一种蚁群优化算法。Apriori算法创建关联规则。基于蚁群算法的阈值,确定最弱规则集,并对关联规则进行约简,找到比先验质量更高的规则。通过优化和改进蚁群算法生成的规则,减少对数据集的扫描。
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