Hi Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target variable and variables from behavioural data as input variables.
B) Using profile data (customers demographic data ) 1. Clustering: Cluster profile data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target variable and variables from profile data as input variables. C) Using profile data (customers demographic data ) and deciles created based on behaviour 1. Deciles: Deciles customers to 10 groups based on some behavioural data 2. Decision Tree: Using rpart classification for generating rules for segmentation using Deciles as target variable and variables from profile data as input variables. In first two cases A and B decision tree model using rpart finish the execution in a minute or two, But in third case (C) it continues to run for infinite amount of time( monitored and running even after 14 hours). fit <- rpart(decile ~., method="class", data=dtm_ip) Is there anything wrong with my approach? Thanks for the help in advance. -Ajit -- View this message in context: http://r.789695.n4.nabble.com/Decision-tree-model-using-rpart-classification-tp3989162p3989162.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
