摘要
挖掘语言值关联规则是数量型属性关联规则中的一个重要研究内容。已有的语言值关联规则挖掘算法没有充分考虑隶属度的信息,为此改进了语言值关联规则的挖掘算法,此算法能充分考虑隶属度的信息,但算法的效率不高。为了提高挖掘算法的效率,通过引入可变阈值,并提出折衷的语言值关联规则挖掘算法,折衷的算法损失了少量的隶属度信息,但节省了挖掘所需的内存和时间。
Mining linguistic valued association rules is an important issue of quantitative association rules. The information of membership hasn抰 been enough considered in the mining algorithm that we proposed before, so we propose the improved algorithm for mining linguistic valued association rules. In this algorithm we enough consider the information of membership, but the algorithm efficiency is not so high. In order to enhance the efficiency, a variable threshold is introduced and we propose an eclectic algorithm for mining linguistic valued association rules. The eclectic algorithm can save memory and time at the cost of losing few information of membership.
出处
《系统仿真学报》
CAS
CSCD
2002年第9期1130-1132,共3页
Journal of System Simulation
基金
国家自然科学基金重点项目(编号 69931040)资助。
