Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

Table 7

Parameters considered for the comparison.

Algorithms	Parameters
ARMGA	PopSize=100, =100, k=2, =0.75, =0.7, =0.1, α=0.01
ARMGSA	PopSize=40, =70, BW=0.05, α1=0.8, α2=0.1
ARMMGA	PopSize=100, =50,000, k=2 (3 with HH), =0.95, =0.85, =0.01, db=0.01
MOPNAR	=100,000, H=13, m=3, PopSize=100, T=10, δ = 0.9, = 2, γ = 2, =0.1, α=5%
QAR-CIP-NSGA-II	PopSize = 100, =50,000, = 0.1, δ=2, α=5%
Apriori	minSup=0.1, minConf=0.75
BPSO-ARM	PopSize=50, Neval=100, c1=2, c2=2, =0.6, =0.1, MaxSimi=0.9