Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing process. The purpose of this paper is to model the recognition of defect type patterns and prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.

Abstract
 1. 서 론
 2. 불량항목별 예측방법
  2.1 예측방법론
  2.2 사전처리 단계(DH : Data Handling)
  2.3 불량항목별 분석단계 (DTA : Defect Type Analysis)
  2.4 불량항목별 예측단계(DTP : Defect Type Prediction)
 3. 사례연구
  3.1 공정소개
  3.2 사전처리 단계(변수선택)
  3.3 불량항목별 분석단계
  3.4 불량항목별 예측단계
  3.5 수행도 비교
 4. 결 론
 참고문헌

• 변성규(한양대학교 산업공학과)
• 강창욱(한양대학교 정보경영공학과)