【作者】 周化；

【导师】 王加阳；

【作者基本信息】 中南大学 ， 计算机软件与理论， 2004， 硕士

【摘要】 本论文的研究工作，主要围绕着基于粗糙集理论的动态约简和完成约简后决策规则的如何制定展开。涵盖了粗糙集理论的基本原理、不同决策表抽样策略的分析、如何利用差别矩阵和差别函数对决策表进行约简等内容。形成了较为完善的决策表动态约简理论和技术方案。 针对不同决策表约简的特殊要求，分析了现有约简方法的优缺点。通过引入动态约简的概念，用动态约简方法处理决策表相当于在决策表约简前预先对决策表进行了抽样处理，提高了约简的精度。并且动态约简挑选出各子决策表中相对稳定的约简作为最终结果，这种处理方法也提高了决策规则的稳定性和描述能力。 在对不同决策表随机抽样方法的研究中，采用了两种新的抽样策略，并详细阐述了此两种抽样策略的适用范围和具体优势。结合概率抽样策略和差别矩阵构造出一个动态约简算法。 从制定决策规则的角度，比较了当前求取决策规则的一些方法，并详细阐述了各方法的优缺点。详细介绍了两种根据决策表直接求取决策规则的方法，以及如何由动态约简计算决策规则。最后建立了一种决策规则的修正算法，完善了决策规则制定后的误差修正。 总结全文，粗糙集理论与方法对于处理复杂系统不失为一种较为有效的手段，被广泛应用于数据挖掘(DM)和数据库知识发现(KDD)领域中。而动态约简及其由约简结果制定决策规则的方法更能为以上两个领域提供高效、精确的数据保证。更多还原

【Abstract】 The research of this thesis is mainly about the dynamic reduct based on rough set theory as well as how to establish and develop the decision rules after completing the reduct. This paper also covers the basic principle of rough set, the analysis of the sample strategy on different decision tables and how to make use of the discernibility matrix and discernibility function to do reduction with the decision table. Thus a relatively perfect theory and technique scheme of dynamic reduct of the decision table is formed.According to specific requirement of the reduct of different decision tables, the concept of dynamic reduct is introduced in this dissertation through analysis on current method of reduct.Using the method of dynamic reduct to deal with the decision table, is equal to take sample from the decision table in advance before the reduction of decision table, which increases the accuracy of reduction. And the relatively stable reduct are chosen from each decision subtable as the ultimate results, which also increases stability and description ability of the decision rules.At the aspect of randomly choose a sample towards different decision tables, this paper puts forward two kinds of new sampling strategy, and expatiates their application scope and concrete advantages. Combining the max rate sampling strategy and the discernibility matrix together creates the dynamic reduct algorithm.From the angle of establishing decision rules, this paper compares current methods on accessing the decision rules, and expatiates their merit and shortcoming. The paper emphasizes on two kinds of methods on directly accessing the decision rules with decision table and how to calculate decision rules with dynamic reduct. Finally, a modified algorithm of decision rules is mentioned, which perfect the error modification after the decision rules are established.In brief, the rough set theory and method is quite an efficient means to deal with complex system, which is applied to the field of dataABSTRACTmining(DM) and knowledge discovery in database(KDD). Moreover, dynamic reduct and the method on establishing the decision rules resulted from it can offer more efficient and precise data for the two fields mentioned above.更多还原