基于粗糙属性向量树的规则提取快速矩阵算法

Fast Matrix Computation Algorithms Based on RAVT for Rules Extraction

本文首先探讨了粗糙集中等价矩阵的基本概念及其运算性质.借助于粗糙属性向量树(RAVT)的巧妙构造,提出了两种能同时完成属性约简、数据清洗和规则提取的快速递推矩阵算法(RMC)和分布式并行矩阵算法(PMC).上述算法强调规则提取的实用性和高效性,通过一个简单实例研究验证了PMC算法的可行性,通过对算法复杂度的深入分析和一组对比实验验证了RMC算法对知识发现、基于数据建模和控制的有效性.

The concept of equivalence matrix and its operation are discussed in this paper. Based on a Rough Attribute Vector Tree （RAVT）, two kinds of fast matrix computation algodthms--Recursive Matrix Computation （RMC） method and Parallel Matrix Computation （PMC） method are proposed for data cleaning and rules extraction finished synchronously in rough information system. The algorithms emphasize the practicability and efficiency of rules generation. A case study of PMC is analyzed in detail and its feasibility is shown, and a comparison experiment on computational complexity of RMC algorithm shows that it is valuable for knowledge discovery and knowledge-based modelling and control.