A novel multi-objective firefly algorithm for optimization of association rules
S. Neelima, N. Satyanarayana, Pannala Krishna Murthy · 2017
Discovering interesting rules has become a basis for proficient decision making from past decades. Numbers of mining techniques are used for generating association rules. Apriori is one of the mining techniques for discovering association rules. Optimization of rules has become an important task as apriori generates plenty of rules. In this paper, Multi Objective Adaptive Weight Firefly (MOAWF) algorithm is proposed for association rule optimization and the proposed algorithm is applied on the rules generated using apriori. Here association rule mining is viewed as a multi-objective problem instead of single objective. Confidence, support and comprehensibility are considered for measuring multi-objective. The performance of proposed algorithm is better when compared to the existing algorithms.