customer_segmentation {creditmodel} | R Documentation |
Customer Segmentation
Description
customer_segmentation
is a function for clustering and find the best segment variable.
Usage
customer_segmentation(
dat,
x_list = NULL,
ex_cols = NULL,
cluster_control = list(meth = "Kmeans", kc = 2, nstart = 1, epsm = 1e-06, sf = 2,
max_iter = 100),
tree_control = list(cv_folds = 5, maxdepth = kc + 1, minbucket = nrow(dat)/(kc + 1)),
save_data = FALSE,
file_name = NULL,
dir_path = tempdir()
)
Arguments
dat |
A data.frame contained only predict variables. |
x_list |
A list of x variables. |
ex_cols |
A list of excluded variables. Default is NULL. |
cluster_control |
A list controls cluster. kc is the number of cluster center (default is 2), nstart is the number of random groups (default is 1), max_iter max iteration number(default is 100) .
|
tree_control |
A list of controls for desison tree to find the best segment variable.
|
save_data |
Logical. If TRUE, save outliers analysis file to the specified folder at |
file_name |
The name for periodically saved segmentation file. Default is NULL. |
dir_path |
The path for periodically saved segmentation file. |
Value
A "data.frame" object contains cluster results.
References
Bezdek, James C. "FCM: The fuzzy c-means clustering algorithm". Computers & Geosciences (0098-3004),doi: 10.1016/0098-3004(84)90020-7
Examples
clust = customer_segmentation(dat = lendingclub[1:10000,20:30],
x_list = NULL, ex_cols = "id$|loan_status",
cluster_control = list(meth = "FCM", kc = 2), save_data = FALSE,
tree_control = list(minbucket = round(nrow(lendingclub) / 10)),
file_name = NULL, dir_path = tempdir())