Mining equalized association rules from multi concept layers of ontology using Genetic Network Programming
Guangfei Yang, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu · 2007
In this paper, we propose a Genetic Network Programming based method to mine equalized association rules in multi concept layers of ontology. We first introduce ontology to facilitate building the multi concept layers and propose Dynamic Threshold Approach (DTA) to equalize the different layers. We make use of an evolutionary computation method called Genetic Network Programming (GNP) to mine the rules and develop a new genetic operator to speed up searching the rule space. The simulation results show that our method could efficiently find some rules even in the early generations.