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1)  association rules extraction
关联规则提取
1.
The intension of each node of the concept lattice is virtually one largest item set,and it is benefit for the association rules extraction.
为了充分利用概念格实现关联规则提取,提出了一种新的概念格结构:加权概念格,并给出其渐进式构造算法及基于该格结构上的关联规则提取算法,同时给出算法分析以说明其可行性。
2)  relationship data mining
关联规则获取
3)  rule extraction
规则提取
1.
Raw cotton yarn tenacity's rule extraction based on Rough Set theory;
基于Rough Set理论对原棉纱线强度的规则提取
2.
Data enrichment and rule extraction based on approximate attribute reduction in the context of rough sets;
基于近似属性约简的信息表浓缩和规则提取
3.
Algorithm on rule extraction based on rough set and neural network theory;
基于粗糙集和神经网络理论的规则提取算法
4)  rules extraction
规则提取
1.
An rules extraction algorithm of decision tree based on rough set theory;
基于粗糙集的决策树规则提取算法
2.
An algorithm of minimum rules extraction based on rough set theory;
基于粗糙集的最小规则提取算法
3.
Cloud model based rules extraction algorithm;
基于云模型的规则提取算法研究
5)  rule extract
规则提取
1.
A program is designed based on arithmetic of rough set theory for decision-rule extracting and parameter reduct in fault infor-mation table of engine.
利用基于粗糙集的知识约简和决策规则提取算法,将柴油机故障信息值进行约简,求出其决策规则,完成柴油机的故障信息条件属性值约简和决策规则提取程序设计。
6)  rule acquisition
规则提取
1.
According to the characteristics of time-series data, a strategy on data attribute reduction and rule acquisition using the concept of prediction success based on rough entropy was proposed.
分析了近似质量在提取非确定性规则方面的不足,并基于粗糙熵的预测成功度概念,结合时序数据特点,提出一种属性约简及规则提取策略。
2.
By these analysis,approaches to rule acquisitions and attribute reductions on a decision formal context are established.
在研究了概念格形成的偏序结构的基础上,引进了由两个形式背景形成的决策形式背景,讨论了外延集类上等价关系和交一致关系,给出了决策形式背景下规则提取与属性约简方法。
3.
The methods of the attribute reduction and a method of the rule acquisition of the consistent decision table a.
讨论了协调决策表的数据约简,并证明其不影响进一步的属性约简和规则提取。
补充资料:Gilliland关联
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性质:1940年E. R. Gilliland以50组二元及多元蒸馏的逐板计算结果为基础,提出精馏塔中回流比与分离所需理论塔板数的关联。在此关联中,以(N-Nm)/(N+1)为纵坐标,(R-Rm)/(R+1)为横坐标作图得出一条关联曲线,N为理论塔板数;Nm为最小理论塔板数;R为回流比L/D,L为塔内回流量,mol;D为塔顶产品量,mol;Rm为最小回流比。1972年V. K. Mdokanov等人曾将此关联曲线近似的用下式表示: 其中

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