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A study on decision tree creation using intervening variable  

Cho, Kwang-Hyun (Department of Early Childhood Education, Changwon National University)
Park, Hee-Chang (Department of Statistics, Changwon National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.4, 2011 , pp. 671-678 More about this Journal
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.
Keywords
Association rule; data mining; decision tree; intervening variable; multi intervening association rule;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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