Data Mining from Large Data Set by Generalized Tree Regression Model

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Our research project dealt with tree regression models as data mining method. We studied the below subjects as generalization of the ordinal tree regression model.(a) Generalization of the covariates use as additive termWe propose the method to estimate tree models which have linear regression terms in each node of regression tree, using degree of interaction effect as splitting criterion. The method enable us to make a simple tree which have fewer splits than ordinal tree regression models.(b) Generalization of distribution assumption of response variableOrdinal tree regression model assume implicitly that response variable distributes as normal distribution. To apply the models to the service time response data, we propose the method under the assumption that response variable has Poisson compound exponential distribution.(c) Varidity study of the tree regression modelWe analyze empirical data to confirm the varidity of proposed methods, for example, Long-term care service time data which was investigated for the Care Needs Certification in Japanese Long-term Care Insurance.The reduction of calculation time remains as future study.