  In this work, we develop a method named Twinning, for partitioning a dataset
into statistically similar twin sets. Twinning is based on SPlit, a recently
proposed model-independent method for optimally splitting a dataset into
training and testing sets. Twinning is orders of magnitude faster than the
SPlit algorithm, which makes it applicable to Big Data problems such as data
compression. Twinning can also be used for generating multiple splits of a
given dataset to aid divide-and-conquer procedures and $k$-fold cross
validation.
