文章首先通过行业股信息表描述决策逻辑语言及粒子计算，然后讨论了智能数据分析的原理，同时给出具体实例并进行分析。
This paper at first describes a decision logic language and granular computing using trade stock information table, then the principle of intelligent data analysis is discussed, an exa.
首先介绍了决策表、广义信息表的构造及特点，然后给出了求决策表的最小属性约简及最小决策算法的计算方法。
Then, according to the features of the generalized information table, the algorithms of acquiring minimal attribute reduction, attribute value reduction and minimum decision algorithm are put forward.
提炼并归纳了“决策表问题”对应的数学计算公式——特殊的多元分段函数，并指出该类分段函数的特点。
The paper summarizes the math formula of decision-making table problem and points characteristic, which a sort of multi-items subsection function owns.
利用该方法计算决策表局部最小确定性，并以此为阈值来控制规则集生成的数量，避免不必要的冗余规则的生成。
As a threshold, the local minimal certainty of decision table is calculated to control the number of rule set generated and avoid redundant rules existing.
该方法不需要改变原始不完备故障诊断决策表的规模，且具有更高的约简计算效率。
The method proposed does not require a change in the size of the original incomplete data set, and has higher efficiency of computing reduction.
模糊控制器采用离线计算、在线查询决策表的方法实现，易于工程实现。
Fuzzy controller is implemented by way of out-line computing and on-line controlling because this method can be easily realized in a project.
文章首先通过行业股信息表描述决策逻辑语言及粒子计算，然后讨论了智能数据分析的原理，同时给出具体实例并进行分析。
Constructs an information table, then describes a decision logic language and granular computing using the information table, at the same time an example is given and data is analyzed.
只需简单更改二进制可辨矩阵的结构，就可以计算动态变化的决策表的核属性，并通过实例验证了该方法的正确性和有效性。
Only modifying the structure of binary discernable matrix simply, it is easy to compute the core of a changeable decision table. The method is demonstrated t...
通过理论分析、具体的实例和UCI数据集验证，该算法可以确保得到决策表的一个约简，并能减少计算量，提高计算速度。
Theoretical analyses, experimental results and UCI dataset show that the algorithm not can get a reduction, but reduces the computing effort and improve the computing efficiency.
通过理论分析、具体的实例和UCI数据集验证，该算法可以确保得到决策表的一个约简，并能减少计算量，提高计算速度。
Theoretical analyses, experimental results and UCI dataset show that the algorithm not can get a reduction, but reduces the computing effort and improve the computing efficiency.
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