A Study Of Database Based On The Rough Set Theory And Granular Computing Theory

Rough set theory, introduced by Z.Pawlak in the 1980s, is a mathematical tool of analyzing data, and the granular computing is a new soft computing method. In this thesis, based on the rough set theory and granular computing theory which are very popular in the field of artificial intelligence, several fundamental theories as well as application problems of rough relational database and relational database are investigated systematically and in-depth, and some innovative conclusions are obtained:Based on the rough set data analysis, the relationships between relational database theory and rough set theory are analyzed across-the-board, at the same time, the question why rough set theory can be applied in relational database is interpreted.Based on rough set theory and relational database theory, the query principle of rough relational database is studied. Rough relational database model as well as rough relational operators are analyzed and improved at first, meanwhile, rough relational database model are compared with common relational database model. Then we study the rough data querying of rough relational database based on rough set theory. As a result, the rough data querying methods are grouped into three kinds: crisp querying, rough complete querying and rough combinatorial querying.Two creative point about the thesis is:In this thesis, we discuss the attribute reduction of rough set from the viewpoint of granular computing, as a result, two new algorithms of attribute reduction are developed. Furthermore, the complexity of algorithms is analyzed simply.The thesis introduced the clustering method simply, in addition, aiming at most existing clustering algorithms that only handle the numeric data or categorical data rather than the mixed data, a clustering algorithm based information granularity was proposed under the framework of the rough set theory. Experiment result shows that the algorithm is effective and feasible.