这就是数学规则学习的实质。
规则学习模块通过自组织聚类过程自动生成规则。
In the rules learning mode, the rules can be produced automatically through the cluster process.
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.
摘要：规则学习算法通过学习样本产生规则集，如伺判断规则集的好坏？
Absrtact: the rule extraction algorithm produces the rule set by learning examples. How to evaluate the rule set?
仿真实验表明经过规则学习，该模型具有了一定的火线形状的自纠正能力。
The experiment shows that with rule learning the model successfully revises the shape of front line whenever obstacle meet.
结合恒力磨削的研究，提出了一种基于磨削数据的模糊控制规则学习方法。
Combined with the study of constant force grinding, a self-study method on fuzzy control rules in grinding process is suggested.
在对归纳学习理论深入研究的基础上，将规则学习算法应用到入侵检测建模中。
Based on the in-deep research on inductive learning theory, a rule learning algorithm is applied in building the intrusion detection model.
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.
采用学习的方式来进行规则的生成，这种规则学习的方式使系统的可移植性大大增强。
The rules are made up by the method of rules learning, which can strengthen the portability of the system.
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.
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.
同时提出，增强抢前场篮板球和防守的能力、加强规则学习、提高年轻队员的实力，是解放军队亟待解决的问题。
But PLA team has to improve the ability of the front backboard recovery and the defense, strengthen the rules study, and develop the abilities and skills of all young players.
实践表明，规则学习算法用于韵律结构预测达到了90％以上的正确率，优于目前其他方法的结果，是一种行之有效的办法。
The experiments show that the rule-learning approach can achieve a better accuracy rate of 90% than the others. Thus it is justified as an effective way to prosodic structure prediction.
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.
实验结果表明：现有算法以16%的有效扩展规则覆盖了93%的标注正例，并使预期精度从51%提高到81%，显示了这套规则学习和评价方法的有效性。
Test results indicate that the algorithm can acquire about 16% of the useful expanded rules to cover 93% of the annotated positive examples and can improve the expected accuracy from 51% to 81%.
实验结果表明：现有算法以16%的有效扩展规则覆盖了93%的标注正例，并使预期精度从51%提高到81%，显示了这套规则学习和评价方法的有效性。
Test results indicate that the algorithm can acquire about 16% of the useful expanded rules to cover 93% of the annotated positive examples and can improve the expected accuracy from 51% to 81%.
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