【摘要】 通过给定的最小支持率和最小信任度来挖掘语言值关联规则往往会得到很多规则 ,因此用户很难获得真正关注的语言值关联规则 .本文提出一种挖掘典型语言值关联规则的算法 ,此算法将挖掘得到的语言值关联规则按照相同的后件进行分组 ,然后对每个分组中的语言值关联规则根据规则之间的不相似性进行聚类 .最后从每个类中挑选出代表类原型的语言值关联规则作为典型的语言值关联规则 .典型的语言值关联规则是语言值关联规则集合中最具有代表意义的规则 .更多还原

【Abstract】 Using the given minimum support and minimum confidence,a large set of association rules with linguistic terms can be discovered and therefore it is difficult for the users to obtain the most interesting ones. An algorithm for mining typical association rules with linguistic terms is presented. In this algorithm,the mined association rules with linguistic terms are grouped by the same consequent,and the association rules with linguistic terms in each sub-group are clustered by the dissimilarity between the rules. The association rules with respect to the medoids are selected as the typical association rules with linguistic terms. These typical association rules with linguistic terms are the most representative rules in the set of association rules with linguistic terms.更多还原