Classification is an important area in a data mining. There are various ways in classification methodologies : the decision tree and the neural network, etc. Recently, Rough set theory has been presented as a method for classification. Rough set theory is a new approach in decision making in the presence of uncertainty and vagueness. In the process of constructing the tree, appropriate attributes have to be selected as nodes of the tree. In this paper, we present a new approach to selection of attributes for the construction of decision tree using the Rough set theory. The suggested method makes more simple classification rules in the decision tree and reduces the volume of the data to be treated.

Abstract
 1. 서 론
  1.1 연구 배경
  1.2 연구 목적
 2. 기존 연구 고찰
  2.1 의사결정나무
  2.2 러프 집합(rough set) 이론
 3. 본 연구에서 제안하는 RSC알고리즘
  3.1 비일관적 데이터의 확률적 처리
  3.2 RSC알고리즘
  3.2 예제
  3.3 기존 알고리즘[9]과 비교
 4. 결 론
 참고 문헌

• 유재진(한양대학교 산업공학과)
• 김재련(한양대학교 산업공학과)