关联规则学习是一类新型的知识发现方法，已成为网络安全审计的重要研究方向。

Learning of association rules is a new method to discover new knowledge and has been a significant aspect of the study on network security audit.

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最后根据改进的关联规则学习算法的核心思想，对网络安全审计模型进行了设计，并给予实现。

Finally, basing on the core idea of improved association rule learning algorithms, this thesis designs and achieves simple models of network security audit.

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实验结果表明，改进的关联规则学习算法在网络安全审计的自适应能力上有较好表现，取得了预期效果。

The experiments show that the improved learning algorithm of association rules in network security audits have better performance of automatic adaptive capacity, achieving the expected effect.

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数据挖掘通常涉及到一些标准的任务，包括聚集、分类、回归分析和关联性规则学习。

Data mining commonly involves a few standard tasks that include clustering, classification, regression, and associated rule learning.

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关联性规则学习(Association rule learning)搜寻数据对象之间的关系，以做出预言、定位产品，等等。

Association rule learning searches for relationships between data objects to make predictions, position products, and so on.

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关联性规则学习(Association rule learning)搜寻数据对象之间的关系，以做出预言、定位产品，等等。

Association rule learning searches for relationships between data objects to make predictions, position products, and so on.

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