Evolutionary and immune algorithms applied to association rule mining

Abstract

Association rule mining is one of the most important data mining tasks. It corresponds to the determination of rules that associate items to other items in a data set, where the items are attributes in transactional databases. Although evolutionary algorithms have been used in this task for some time, there are few applications of immune algorithms to such problem. This paper presents one typical genetic algorithm plus two clonal selection algorithms applied to association rule mining under the perspective of several measures of interest. © 2012 Springer-Verlag.