Ranking of Association Rules Toward Smart Decision for Smart City

Ranking of association rules based on their significance in knowledge discovery has become an important issue in data mining. Traditional mining algorithms may often generate a huge number of rules including less or non-significant ones. Users expect is to deal with the most significant association rules to get a smart decision. Ranking express the level of significance which may reduce the confusion in decision making. This paper introduces Gravity as a measure of rule significance and henceforth ranks the association rules. Experimental analysis shows the effectiveness of the proposed technique.