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

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。

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Based on the in-deep research on inductive learning theory, a rule learning algorithm is applied in building the intrusion detection model.

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。

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This paper proposes a method based on granularity computing to get the sentiment classification of text comments through rule learning by establishing the granule network.

提出了一种基于粒运算的方法，通过建立粒网络生成分类规则，从而得到评论文本的情感倾向分类。

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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.

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。

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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.

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。

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