MicroRNAs precursors with stem-loop structure play a significant role in forming mature miRNAs.This paper aims to identify conserved structure patterns with association rule mining,by which to explore the regulatory characteristics of miRNAs.The secondary structure of miRNAs is modeled by stem-loop substructure as well as a flanking region.Support constraints are proposed to specify the generation of interesting frequent itemsets.Further,a novel measure is applied to cluster interesting frequent itemsets.These avoid generating redundant rules and enhance the efficiency of data mining.%miRNA前体（pre-miRNAs）是产生成熟miRNAs的基因表达产物,能够形成较为稳定的茎—环（stem-loop）结构.为了深入研究miRNAs,对miRNAs前体的二级结构建模,并采用关联规则识别miRNAs基因在进化过程中的结构模式.通过分析产生的结构—类别关系（structure-category relationships）来探索miRNAs的功能特征和调节机制.为了获得较好的实验结果,改进传统的关联规则挖掘算法,应用支持度约束和频繁项集的聚类来优化数据挖掘结果.
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