In this paper, we propose a two-stage multi-objective fuzzy mining algorithm for dealing with linguistic knowledge discovery. In the first stage, the multi-objective genetic lgorithm is used to derive a set of non-dominated membership functions (Pareto solutions) with two objective functions. In the second stage, the clustering technique is utilized to find representative solutions from the Pareto solutions. The epresentative solutions could be employed to mine fuzzy association rules according to the favorites of decision makers. Experiments on a simulation dataset are made and the results show the effectiveness of the proposed algorithm.
