【作者】 李祝平；

【导师】 冯秀芳；

【作者基本信息】 太原理工大学 ， 计算机应用， 2005， 硕士

【摘要】 随着数据库技术的成熟,数据应用的普及,以及互联网的高速发展,人类积累的数据量正在以指数级速度迅速增长。传统的数据分析和查询方法已不能满足人们对隐藏在数据背后知识的迫切需要,在这种信息需求的强劲推动下,知识发现和数据挖掘应运而生,而粗糙集理论作为一门新的数学工具,凭借它不需要附加任何外界信息或先验知识这一特点,突破了其它数据分析工具的局限,避免了人的主观因素对数据挖掘结果的影响,逐渐成为了研究知识发现的重要的数学工具之一。而属性约简是基于粗糙集理论的数据挖掘模型中的关键步骤,同时也是粗糙集理论研究中的一个研究重点,因此本文的重点主要是针对属性约简算法进行改进研究。 首先,本文介绍了经典粗糙集的基本理论和模型及其实际应用,然而经典粗糙集模型的一个局限性是它所处理的分类必须是完全正确的或肯定的,因而它的分类是精确的,即只考虑完全“包含”与“不包含”,而没有某种程度上的“包含”与“属于”,而实际应用中噪声数据又是不可避免的。经典粗糙集模型更多还原

【Abstract】 With the rapid development of database techniques and computer network, large amount of data are stored, the rapid growth demand for extracting, understanding and assimilating useful knowledge from the growing mountains of data outpaces the traditional methods of data analysis, which leads to the emerging of knowledge discovery in databases and data mining. Rough set theory is a new mathematic tool and it has no need of other existing information, which makes it overcome shortcoming of other methods and avoid the influence of subjective factor to the results of data mining. It becomes one of primary methods of KDD.First, we introduce the rough set theory and its model. Theclassical model has limitation in dealing with inconsistent information. The classification by rough set must be entirely right or positive. Therefore its classification is accurate, namely only consider totally "including" and not "including", and have not a certain extent "include" and "belong to", but the noise data are unavoidable in practical application. Another limitation of the model is the target that it dealt with is already known, and the conclusion got from the model is only suitable for these targets , but in practical application, often need to apply the conclusion got from the small-scale targets to the extensive targets. The limitation of the classical model limits the application of it. Consulting VPRS (Variable precision rough set model) and changing the definition of precision with real classification accuracy, we propose one generalized model with real classification accuracy.This paper has deep research in the algorithms of attribute reducts and summarizes some present main algorithms. But up till now, though there are some achievements in the attribute reduct algorithm, there has not a recognized and high-efficient algorithm. The heuristic attribute reduct algorithm based on attributefrequency and another heuristic algorithm based on attribute reliability are two main attribute reduct algorithm based on the importance of attribute. The algorithm based on attribute frequency is a non-abundant algorithm, so it can’t guarantee to get one result finally, but the algorithm based on attribute reliability give the guarantee. In calculation of the attribute importance, the calculation amount based on attribute frequency should be less than the calculation based on attribute reliability degree, so combine two pluses and minuses of algorithm, this paper proposes one improved algorithm of the algorithm based on attribute reliability degree. This algorithm guarantees to get reducts of decision table and save time compare with the original algorithm. Because it can’t be unavoidable to contain the noise data in the decision table in reality, we use the rough set model based on the classification reliability in the improved algorithm. Then it makes this algorithm deal with the decision table with certain noise and having very good cover ability and generalizable ability.We compare the two algorithms from results of reduct and time of reduct by experiments. From the angel of results, the improvedalgorithm finds a reduct finally and it is an improvement of algorithm based on attribute frequency. From the angel of time, time is less than the algorithm based on attribute reliability. Finally, we deal with data with certain noise data, and have found reducts, the experiment is proved, and the improved algorithm has certain fault-tolerant ability.更多还原