摘要
The concepts of Rough Decision Support System (RDSS) and equivalence matrix are introduced in this paper. Based on a rough attribute vector tree (RAVT) method, two kinds of matrix computation algorithms — Recursive Matrix Computation (RMC) and Parallel Matrix Computation (PMC) are proposed for rules extraction, attributes reduction and data cleaning finished synchronously. The algorithms emphasize the practicability and efficiency of rules generation. A case study of PMC is analyzed, and a comparison experiment of RMC algorithm shows that it is feasible and efficient for data mining and knowledge-discovery in RDSS.
The concepts of Rough Decision Support System （RDSS） and equivalence matrix are introduced in this paper. Based on a rough attribute vector tree （RAVT） method, two kinds of matrix computation algorithms— Recursive Matrix Computation （RMC） and Parallel Matrix Computation （PMC） are proposed for rules extraction, attributes reduction and data cleaning finished synchronously. The algorithms emphasize the practicability and efficiency of rules generation. A case study of PMC is analyzed, and a comparison experiment of RMC algorithm shows that it is feasible and efficient for data mining and knowledge-discovery in RDSS.
基金
ThisworkwassupportedinpartbytheNational973KeyFundamentalResearchProjectofChina(2002CB312200),theNational863HighTechnologyProjectsFoundationofChina(2002AA412010),andtheNationalNaturalScienceFoundationofChina(60174038).
