Mining confident minimal rules with fixed-consequents

Association rule mining (ARM) finds all the association rules in data, that match some measures of interest such as support and confidence. In certain situations where high support is not necessarily of interest, fixed-consequent association-rule mining for confident rules might be favored over traditional ARM. The need for fixed consequent ARM is becoming more evident in a number of applications such as market basket research (MBR) or precision agriculture. Highly confident rules are desired in all situations; however, support thresholds fluctuate with the applications and the data sets under study, as we shall show later. We propose an approach for mining minimal confident rules in the context of fixed-consequent ARM that relieves the user from the burden of specifying a minimum support threshold. We show that the framework suggested herein is efficient and can be easily expanded by adding new pruning conditions pertaining to specific situations.