러프집합과 계층적 구조를 이용한 규칙생성 = Rule Generation using Rough Sets and Hierarchical Structure

다국어 초록 (Multilingual Abstract)

This paper deals with the rule generation from data for control system and data mining using rough set which is an effective technique in extracting knowledge from incomplete and inconsistent information. If the cores and reducts are searched for without consideration of the frequency of inconsistent data belonging to the same equivalent class in rough set, unfortunately the necessary attribute may be discarded, or the unnecessary attributes may not be discarded. In consequence, the resultant rules don't fit well the characteristics of the data. To improve this, we handle the inconsistent data with a probability measure defined by support. As a result, the effect of the uncertainty in the knowledge reduction can be reduced to some extent. Also to make effective rule base, we construct the rule base in a hierarchical structure by applying core as the classification criteria at each level. When more than one core exist, the coverage degree is used to select an appropriate one among them to increase the classification rate. The proposed method gives more proper and effective rule base in compatibility and size. For some data mining example, simulations are performed to show the effectiveness of the proposed method.