Rules Extraction from Multiple Decisions Ordered Information Tables

Abstract

Ordered information table is one of the most important research areas of granular computing. In this thesis, we introduce multiple decisions ordered information tables based on the concept of ordered information tables. Multiple decisions ordered information tables are used to describe the actual multiple decision attributes situation of reality. We study the process of rule extraction from multiple decisions ordered information tables thoroughly and several concepts about this process are proposed and discussed. At last, an example of multiple decisions ordered information tables is used to illustrate the basic ideas. These ideas and methods are quite useful for KDD, DM and GC.