Abstract
In this paper, we formulated association rule mining as a combinatorial, multi-objective global optimization problem by considering measures such as support, confidence, coverage, comprehensibility, leverage, interestingness, lift and conviction. Here, we developed three evolutionary miners viz., Multi-objective Binary Particle Swarm Optimization based association rule miner (MO-BPSO), a hybridized Multi-objective Binary Firefly Optimization and Threshold Accepting based association rule miner (MO-BFFOTA), hybridized Multi-objective Binary Particle Swarm Optimization and Threshold Accepting based association rule miner (MO-BPSOTA) and applied them on various datasets and conclude that MO-BPSO-TA outperforms all others.
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