매개 변수를 이용한 의사결정나무 생성에 관한 연구 = A study on decision tree creation using intervening variable

다국어 초록 (Multilingual Abstract)

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.

참고문헌 (Reference)

1 최승배, "웹마이닝 기법을 이용한 학과 홈페이지 분석" 한국자료분석학회 13 (13): 317-329, 2011

2 조광현, "연관성규칙에서의 다중매개관계에 관한 연구" 한국자료분석학회 13 (13): 297-306, 2011

3 이윤성, "고객세분화에서 TRFM을 반영한 고객생애가치모형의 개발" 한국자료분석학회 12 (12): 3271-3282, 2010

4 Agrawal, R., "Mining association rules between sets of items in large databases" 207-216, 1993

5 박희창, "Discovery of Association Rules Using Latent Variables" 한국데이터정보과학회 17 (17): 149-160, 2006

6 김민환, "Development of Component Association Rules and Macro Algorithm" 한국데이터정보과학회 19 (19): 197-207, 2008

7 Hartigan, J. A, "Clustering Algorithms" John Wiley & Sons 1975

8 Breiman, L., "Classification and regression trees" Chapman & Hall/CRC 1984

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

10 이근우, "A Study for Statistical Criterion in Negative Association Rules Using Boolean Analyzer" 한국데이터정보과학회 19 (19): 569-576, 2008

11 박희창, "A Study for Antecedent Association Rules" 한국데이터정보과학회 17 (17): 1077-1083, 2006