An improved approach to find membership functions and multiple minimum supports in fuzzy data mining
Expert Systems with Applications: An International Journal
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In this paper, we propose a novel mining approach based on the genetic process and an evaluation mechanism to automatically construct an effective fuzzy rule base. The proposed approach consists of three phases: fuzzy-rule generating, fuzzy-rule encoding and fuzzy-ruleevolution. In the fuzzy-rule generating phase, a number of fuzzy rules are randomly generated. In the fuzzy-rule encoding phase, all the rules generated are translated into fixed-length bit strings to form an initial population. In the fuzzy-rule evolution phase, genetic operations andcredit assignment are applied at the rule level. The proposed mining approach chooses good individuals in the population for mating, gradually creating better offspring fuzzy rules. A concise and compact fuzzy rule base is thus constructed effectively without human expertintervention.