주변조건부 변수를 이용한 의사결정나무모형 생성에 관한 연구 = A study on decision tree creation using marginally conditional variables

다국어 초록 (Multilingual Abstract)

Data mining is a method of searching for an interesting relationship among items in a given database. The decision tree is a typical algorithm of data mining. The decision tree is the method that classifies or predicts a group as some subgroups. In general, when researchers create a decision tree model, the generated model can be complicated by the standard of model creation and the number of input variables. In particular, if the decision trees have a large number of input variables in a model, the generated models can be complex and difficult to analyze model. When creating the decision tree model, if there are marginally conditional variables (intervening variables, external variables) in the input variables, it is not directly relevant. In this study, we suggest the method of creating a decision tree using marginally conditional variables and apply to actual data to search for efficiency.

참고문헌 (Reference)

1 박희창, "향상도 영향 감소화에 의한 연관성 순위결정함수" 한국데이터정보과학회 21 (21): 397-405, 2010

2 조광현, "외적 변수를 이용한 의미 없는 연관성 규칙 발견" 한국자료분석학회 13 (13): 1343-1351, 2011

3 조광현, "연관성규칙에서 의미 없는 규칙의 발견에 관한 연구" 한국데이터정보과학회 22 (22): 81-88, 2011

4 조광현, "매개 변수를 이용한 의사결정나무 생성에 관한 연구" 한국데이터정보과학회 22 (22): 671-678, 2011

5 조광현, "다중외적연관성규칙을 이용한 불필요한 입력변수 제거에 관한 연구" 한국데이터정보과학회 22 (22): 877-884, 2011

6 Hartigan, J. A., "Clustering algorithms" John Wiley & Sons 1975

7 Breiman, L., "Classification and regression tree" Wadsworth and books 1984

8 Quinlan, J. R., "C4.5 programs for machine learning" Morgan Kaufmann Publishers 1993