There are a wide variety of decision-tree construction algorithms, and we
outline the distinguishing features of a few of them. See the bibliographical notes
for details. With very large data sets, partitioning may be expensive, since it
involves repeated copying. Several algorithms have therefore been developed to
minimize the I/O and computation cost when the training data are larger than
available memory.