SBSS.P algorithm is a stochastic algorithm. It obtains data subsets through uniform sampling in each neuron after clustering through SOM neural network, with details given in May et al. (2010).
SBSS.P(data, control)Return the training, test and validation subsets. If the original data are required to be split into two subsets, the training and test subsets can be combined into a single calibration subset.
The dataset should be matrix or Data.frame. The format should be as follows: Column one is a subscript vector used to mark each data point (each row is considered as a data point); Columns from 2 to N-1 are the input data, and Column N are the output data.
User-defined parameter list, where each parameter definition refers to the par.default function.
May, R. J., Maier H. R., and Dandy G. C.(2010), Data splitting for artificial neural networks using SOM-based stratified sampling, Neural Netw, 23(2), 283-294.