| 1. | Some properties of positive regions in rough set
中正区域的若干性质 |
| 2. | Positive region deduction of decision table and computation of core
决策表的正域约简及核的计算 |
| 3. | The discernibility matrix is one of the most important approaches to computing positive region , reduct , core and value reduct in rough sets
正区域reduct核以及值reduct等概念源于1982年powlak提出的rough sets 1 。 |
| 4. | A distributed model of incremental attribute reduction is also presented by decomposing values of decision attribute of positive region and boundary region in non - tolerant decision table
此外，通过对不相容决策表的正区域的决策值和边界域对原决策表进行分解，得到了一种分布式增量属性约简模型。 |
| 5. | 5 . in the simulated distribution residual stress map , the results of fe simulation is close to those measured by silicon piezoresistive sensor chip while the distribution of negative region and positive region matches with the measured map
硅压阻传感芯片测量结果与计算机模拟结果的比较表明，硅压阻传感芯片测量值与计算机模拟值很接近，测量值的正负区间与模拟值的正负区间完全吻合。 |
| 6. | Among the decomposed synthesis reasoning sources , the field intensities were defined by dependency and classification methods based on the concept of positive region , by discernibility matrix method based on the rate of appearance , and by mutual information , condition entropy and mutual information gain rate methods based on information theory
在分解得到的综合源中，基于正域的概念，用依赖度和分类质量方法定义了场强，根据属性出现频率， ?用差别矩阵定义了场强，并结合信息论，通过互信息、条件熵和互信息增益率定义了场强。 |
| 7. | Its computational complexity for positive region and reduct is o m 2 - tm n instead of o m - tm n 2 in discernibility - matrix - based approach , and is not over o n 2 for other concepts in rough sets , where m and n are the numbers of attributes and objects respectively in a given dataset also called an - it information system in rough sets
由于reduct是给定信息系统属性集合的一个属性子集， reduct可以考虑为特征选择的一个解答，因此， reduct理论的另一个可能应用是特征选择。无论是情报分析还是特征选择，关于这些应用的一个共同要求是，有关计算reduct核以及reduct理论中其它概念的算法效率必须高。 |
| 8. | To the problem that finding rules in enormous data is very time - consumable and the expansibility of existed algorithms is not very good , the thesis proposes a new method to discompose large data table based on the concepts of positive region and the importance of attribute in rough set theory . existed algorithms of rule deduction can be applied directly on the tree structure obtained by partition and the times for computation will be reduced observably . validation of information entropy on the partition structure shows that the partition of data table will not lead to the loss of information , while the computing speed increases at the same time , which reflects the practicability and rationality about the partition of large data table
针对海量数据处理起来极为耗时，现有算法拓展性较差的问题，基于rough集理论中的集合正域概念以及由此定义的属性重要性概念，提出一种大型数据表分解算法，现有的规则归纳算法可直接在分解得到的树型结构上应用，将大大降低知识发现的时间，并从信息理论的角度利用信息熵概念对该分解结构进行了验证，分析了这种分解的实用性及合理性，揭示了这种分解结构在提高计算速度的同时不会损失信息量。 |
