A rough rules mining approach based on variable precision rough set theory is proposed in this paper.
提出了一种基于变精度粗糙集理论的规则挖掘算法。
Study shows that this method presents more flexibility and efficiency than the approach of independent mining association rules.
研究表明，同孤立的关联规则挖掘方法相比，该方法具有较大的灵活性和更高的效率。
And making an experiment on it, it proves that binary system sequences set is efficient and feasible as an approach of organization data based on mining of association rules.
通过实验验证，在关联规则数据挖掘中采用二进制序列集这一组织数据方法是有效且可行的。
In change mining phase the approach firstly excavate rules between two different time periods, comparing them to determine the trend changes.
变化挖掘方法是通过建立专利规则，比较两个不同时期的专利并确定其变化趋势。
Therefore, the authors propose a new approach of Augmented Naive Bayesian network based on Association Rules data mining to diagnose faults in power network.
针对以上问题，本文研究采用关联规则属性约简和贝叶斯网络相结合的电网故障诊断方法。
Therefore, the authors propose a new approach of Augmented Naive Bayesian network based on Association Rules data mining to diagnose faults in power network.
针对以上问题，本文研究采用关联规则属性约简和贝叶斯网络相结合的电网故障诊断方法。
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