Open AccessEARMGA and Apriori Algorithm's Performance Evaluation for Association Rule Mining
Author(s) -
Shivani Singh,
Dharani Kumar Talapula
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a2144.109119
Subject(s) - association rule learning , apriori algorithm , data mining , computer science , a priori and a posteriori , gsp algorithm , genetic algorithm , association (psychology) , algorithm , machine learning , philosophy , epistemology
Association rule mining techniques are important part of data mining to derive relationship between attributes of large databases. Association related rule mining have evolved huge interest among researchers as many challenging problems can be solved using them. Numerous algorithms have been discovered for deriving association rules effectively. It has been evaluated that not all algorithms can give similar results in all scenarios, so decoding these merits becomes important. In this paper two association rule mining algorithms were analyzed, one is popular Apriori algorithm and the other is EARMGA (Evolutionary Association Rules Mining with Genetic Algorithm). Comparison of these two algorithms were experimentally performed based on different datasets and different parameters like Number of rules generated, Average support, Average Confidence, Covered records were detailed.