An In-depth Survey Analysis Applying Data Mining Techniques

데이터마이닝을 이용한 설문조사의 심층 분석

  • Published : 2006.12.31


To accomplish the educational objectives of a department, a system for CQI(Continuous Quality Improvement) is necessary. Improving the educational system by survey analysis is one of the most important factors for accomplishing the educational objectives. In general, survey analysis is carried out by using statistical distribution on an attribute or correlation analysis between two attributes. However, these analysis schemes have a limitation that they cannot find relations among various attributes. In this paper, an in-depth survey analysis method applying data mining techniques is presented. Data mining is a technique for extracting interesting knowledges from a large set of data. Survey from undergraduate students in the School of Computing of Soongsil University is analyzed in this paper by using a data mining tool, called Clementine. Results of Clementine analysis show the relationship between 'grade', and other attributes hierarchically, and provide useful information that can be applied in student consulting and program improvement.


  1. 김완섭.이수원(2003). 상품별 구매 패턴을 이용한 프로파일 기반 추천과 협력적 추천과의 결합, 데이터마이닝학원 2003 추계학술대회 논문집, 172-176
  2. 알렉스 버슨 외 지음, 홍성완 외 옮김(2000). CRM을 위한 데이터마이닝. 대청미디어
  3. 장남식, 홍성완, 장재호(1999). 성공적인 지식경영을 위한 핵심 정보기술, 데이터마이닝. 대청미디어
  4. 한경식.이수원(2005). 대용량 데이터를 위한 전역적 범주화를 이용한 결정 트리의 순차적 생성. 한국정보처리학회지 2005년 8월, Vol. 12, 487-498
  5. 허명회.이용구(2004). 데이터마이닝 모델링과 사례, 데이터솔루션
  6. 허준 외(2003). Clementine 7 매뉴얼. 데이터솔류션
  7. Richard J.Roiger & Michael W, Geatz(2003). Data Mining: A Tutorial-Based Primer, Addison-Wesley
  8. Ian H. Witten & Eibe Frank(200S). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann
  9. Jiawei Han & Micheline Kamber(2006). Data Mining Concepts and Techniques. Morgan Kaufmann