Data mining is a new emerging area for the research of artificial intelligence and databases, in which incremental updating of association rules is an important research topic.
数据挖掘是当今国际人工智能和数据库研究的新兴领域，而关联规则的更新是数据挖掘的一个重要研究内容。
Mining and updating maximum frequent patterns is a key problem in data mining research.
挖掘和更新最大频繁模式是多种数据挖掘应用中的关键问题。
An incremental mining technique is proposed in order to solve the problem of frequent updating of moving log database and mine the user mobility patterns.
该文主要对移动日志数据库不断更新的问题，提出了增量挖掘的方法，挖掘用户的移动模式。
A novel algorithm, QAR, for mining quantitative association rules and an incremental updating algorithm, IUQAR, are proposed.
提出了一种新的量化关联规则挖掘算法QAR及其增量式更新算法IUQAR。
This paper proposes a fast algorithm DMFP and an updating algorithm IUMFP, which are based on Prefix Tree for mining maximum frequent patterns.
因此，文章提出了一种最大频繁模式的快速挖掘算法DMFP及更新算法IUMFP。
This framework is based on CIDF, and uses Data Mining to mine intrusion models, then automatically transforms it into intrusion detection rules for rule base's updating.
该系统基于公共入侵检测框架（CIDF）构建，当出现新攻击时，利用数据挖掘对海量数据进行挖掘，得出入侵模型后由系统自动转换为检测规则以实现规则库的自动更新。
This framework is based on CIDF, and uses Data Mining to mine intrusion models, then automatically transforms it into intrusion detection rules for rule base's updating.
该系统基于公共入侵检测框架（CIDF）构建，当出现新攻击时，利用数据挖掘对海量数据进行挖掘，得出入侵模型后由系统自动转换为检测规则以实现规则库的自动更新。
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