The Utilization of Ontology in Association Rule

Abstract

Association rule is one of the data mining techniques to find associative combinations of items. There are several algorithms including Apriori, FP-Growth, and CT-Pro. One of the advantages of the Apriori algorithm is that it produces many rules. To improve its result, one of the methods is by using the semantic web technology. This work proposes how the hierarchical type of ontology can be utilized by the Apriori algorithm to improve the results. The Apriori with ontology implements the Interestingness Rule (IR) which is a parameter to determine the degree of association between combinations of items in a dataset. The series of experiments show that the proposed idea can improve the results compare to the default Apriori algorithm.