Interpreting Low and High Order Rules: A Granular Computing Approach

The main objective of this paper is to provide a granular computing based interpretation of rules representing two levels of knowledge. This is done by adopting and adapting the decision logic language for granular computing. The language provides a formal method for describing and interpreting conditions in rules as granules and rules as relationships between granules. An information table is used to construct a concrete granular computing model. Two types of granules are constructed from an information table. They lead to two types of rules called low order and high order rules. As examples, we examine rules in the standard rough set analysis and dominance-based rough set analysis.