• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.239-247
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• 2003

Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, etc. CHAID(Chi-square Automatic Interaction Detector), is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. In this paper we propose and CHAID algorithm by cube-based sampling and explore CHAID algorithm in view of accuracy and speed by the number of variables.

• 한국데이터정보과학회:학술대회논문집
• /
• 2003.10a
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• pp.215-228
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• 2003

Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, interaction effect identification, category merging and discretizing continuous variable, etc. CHAID(Chi-square Automatic Interaction Detector), is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. CHAID modeling selects a set of predictors and their interactions that optimally predict the dependent measure. In this paper we explore CHAID algorithm in view of accuracy and speed by sampling proportion.