Data mining is widely used for turning huge amounts of data into useful information and knowledge in the information industry in recent years. When analyzing data set with continuous values in order to gain knowledge utilizing data mining, we often undergo a process called discretization, which divides the attribute’s value into intervals. Such intervals from new values for the attribute allow to reduce the size of the data set. In addition, discretization based on rough set theory has the advantage of being easily applied. In this paper, we suggest a discretization algorithm based on Rough set and SOM(Self-Organizing Map) as a means of extracting valuable information from large data set, which can be employed even in the case where there lacks of professional knowledge for the field.

Abstract
 1. 서 론
 2. 러프 집합과 SOM(Self-Organizing Map)
  2.1 러프 집합
  2.2 SOM(Self-Organizing Map)
 3. 연속형 속성의 이산화(Discretization)
  3.1 연속형 속성의 초기 이산화
  3.2 러프소속함수값 계산
  3.3 SOM을 이용한 군집 생성
  3.4 군집 분석을 통한 이산화 구간의 선택
  3.5 실험결과
 4. 결 론
 참고문헌

• 서완석(한양대학교 산업공학과)
• 김재련(한양대학교 산업공학과)