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A fuzzy rule-based classification system (FRBCS) is one of the most popular approaches used in pattern classification problems. One advantage of a fuzzy rule-based system is its interpretability. However, we're faced with some challenges when generating the rule-base. In high dimensional problems, we can not generate every possible rule with respect to all antecedent combinations. In this paper, by making the use of some data mining concepts, we propose a method for rule generation, which candoi:10.1007/978-3-540-77226-2_56 dblp:conf/ideal/FakhrahmadZJ07 fatcat:4owdfevxdvbjrodozeaexlfpdy