A Novel Method for Mining Association Rules from Continuous Attributes Based on Cultural Immune Algorithm

Abstract

It is an important topic to mine association rules from data with continuous attributes. A novel model based on cultural algorithm and immune algorithm is proposed in this paper. This algorithm uses the cultural algorithm framework in which the immune algorithm embedded. The immune algorithm is used to discretize the continuous attributes and search association rules in the database rapidly. The cultural algorithm is used to obtain the commonly accepted beliefs in order to guide and speed up the search. The new algorithm integrates the discretization, attributes reduction and association rules mining together. In addition, a new diversity operator is put forward in order to discover global solutions. The experiment shows that the new algorithm is superior to immune algorithm in convergence speed and the rule’s accuracy.