Aiming at discovering causal relationships between frequent episodes, episode rule mining has been broadly applied in many fields such as sensor data processing, network security monitoring, finance & securities managing, transaction log analyzing, and so on. To mine the non-redundant episode rules from an event sequence, an algorithm called Extractor is proposed in this paper. Extractor discovers all frequent closed episodes and their generators by employing the support definition of both minimal and non-overlapping occurrences and the depth-first search strategy, which assures the quality and efficiency of mining frequent closed episodes and their generators. Moreover, Extractor avoids redundant generator checking by utilizing the Apriori Property of non-generators. In addition, Extractor generates non-redundant episode rules directly from frequent closed episodes and their generators, which improves the quality and efficiency of generating episode rules. Experiments have proved the effectiveness of the proposed method.%情节规则挖掘旨在发现频繁情节之间的因果关联,已广泛应用于传感器数据处理、网络安全监控、金融证券管理、事务日志分析等众多领域.针对一个事件序列上的无冗余情节规则挖掘,提出了算法Extractor.该算法采用最小且非重叠发生的支持度定义和深度优先的搜索策略来发现频繁闭情节及其生成子,保证了频繁闭情节及其生成子的挖掘质量和挖掘效率；利用非生成子情节的Apriori性质,避免了冗余的情节生成子判断；直接由频繁闭情节及其生成子产生无冗余情节规则,提高了情节规则的生成质量和生成效率.所进行的实验证实了该情节规则抽取算法的有效性.
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