Mining Ensemble Association Rules by Karnaugh Map

Generally, the study of association mining is majority concentrate on how to find out the frequent item set and attempt to infer the relationship between them. But very few studies deliberate about the two notable issues. The one is the huge number of association rules, which easily caused the decision maker to get lost in it. The other is the general association rules, which just imply the relationship with ldquoANDrdquo logic between items, but not imply the relation with ldquoORrdquo and ldquoXORrdquo logic between items. In this paper, we apply Karnaugh Map (K-Map) principle to find out ensemble association rules by experiment transaction data, it names dasiaARKMpsila. The experiment result shows that the ARKM approach which not only provides computational efficiency to obtain simplified and usable rules but also manifest adaptive to the decision maker.