为了能够从不完备决策表（IDT）中进行知识发现和数据挖掘，提出一种新的具有对称性的双重可变精度限制容差关系粗集模型（VPLTRST）。
To obtain knowledge and data from incomplete decision table(IDT), the paper presents a new doubly variable precision limited tolerance rough set theory model(VPLTRST).
粗糙集理论作为一种处理不完备信息的有力工具，已广泛应用于人工智能的许多领域，特别是数据挖掘和知识发现领域。
Rough set theory, a powerful tool to deal with incomplete information, has been widely used in the area of artificial intelligence, especially in data mining and knowledge discovery.
基于粗糙集理论的不完备数据分析方法，以可辨识矩阵作为算法的基础，提出了一种改进的不完备数据分析方法。
Based on an incomplete data analysis method of the rough set theory and the distinguish matrix, bring forward an improved ROUSTIDA algorithm.
应用粗集理论可以在决策支持系统中对不完备数据进行分析、推理，提取有用特征，简化信息处理，得出肯定结论。
In the Decision Support System the application of Rough Set could analyze, infer to the incomplete data and pick-up useful character, simplify information processing, and get conclusion in the end.
提出了一种基于粗糙集的不完备信息系统数据填补方法。
This paper brought forward a data packing method of incomplete information system based on rough sets and grey system theory.
本文简要阐述了数据挖掘的基本原理，针对车辆故障诊断的特殊性和复杂性及诊断中存在的不完备信息和不一致信息，阐述了将粗糙集理论用于车辆故障诊断的必要性。
In this paper the basic theory of data mining is briefly introduced. For particularity and complexity of vehicle faults diagnosis, it is necessary to integrate rough set theory with neural network.
本文简要阐述了数据挖掘的基本原理，针对车辆故障诊断的特殊性和复杂性及诊断中存在的不完备信息和不一致信息，阐述了将粗糙集理论用于车辆故障诊断的必要性。
In this paper the basic theory of data mining is briefly introduced. For particularity and complexity of vehicle faults diagnosis, it is necessary to integrate rough set theory with neural network.
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