So, along with further studies of RS, decision rules mining in decision table by using RS has become a hotspot.
因此，随着对粗糙集理论的深入研究，利用粗糙集进行决策表中的决策规则挖掘便成了一个热点课题。
Most data mining tools use rule discovery and decision tree technology to extract data patterns and rules; its core is the inductive algorithm.
大部分数据挖掘工具采用规则发现和决策树分类技术来发现数据模式和规则，其核心是归纳算法。
Most data mining tools for knowledge discovery generally use rule discovery and decision tree technology to extract data patterns and rules.
用于知识发现的大部分数据挖掘工具均采用规则发现和决策树分类技术来发现数据模式和规则。
Then the decision tree and class association rules mining are used on the video attribute database to extract a decision tree classification rule set and a class association rule set respectively.
然后分别使用决策树、分类关联规则等技术对视频属性数据库进行数据挖掘，提取出决策树分类规则集和分类关联规则集；
Data mining is an important problem in KDD, and Rough set as a theory of set with fuzzy boundary is widely applied to infer classification rules from decision system.
数据挖掘是知识发现领域的一个重要问题，粗糙集理论是一种具有模糊边界的数据挖掘方法，它被广泛应用于决策系统的分类规则提取中。
The data mining system can discover association rules and decision rules.
本数据挖掘系统使用增量算法发现关联规则和提取决策规则。
In order to improve the classification quality of decision system, a strategy of data mining classification rules based on rough approaching approximation measurement in data ware is proposed.
为了提高决策系统的分类质量，探讨了一种在数据仓库中基于粗糙逼近近似度量的挖掘分类规则策略。
MDRBR algorithm USES uniform minimum support threshold for mining default decision rules, the method can not effectively mine the interesting default decision rules to users.
MDRBR算法采用单一的规则支持度阈值进行缺省规则的挖掘，这不利于有效地挖掘出用户感兴趣的缺省规则。
This paper gives the rule-support degree a new definition, extends present models and brings forward an algorithm of mining weighted-decision-rules, the result of the experiment shows its effectivity.
通过对规则支持度提出新的定义，对现有的模型进行了扩展，并由此提出了一种新的决策规则挖掘算法，实验结果表明了其有效性。
Finally, valuable decision-making information is discovered by using the parallel algorithm based on FP-growth while mining the association rules of online transactions.
利用该算法对网上交易进行关联规则挖掘，发现了有价值的决策支持信息。
Missing data filling and rules extraction in incomplete decision table are two important data mining problems.
不完全信息系统中遗失数据的补充和规则的提取，一直是数据挖掘技术面临的重要问题。
Missing data filling and rules extraction in incomplete decision table are two important data mining problems.
不完全信息系统中遗失数据的补充和规则的提取，一直是数据挖掘技术面临的重要问题。
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