Decision Trees on Parallel Processors

A framework for induction of decision trees suitable for implementation on shared- and distributed-memory multiprocessors or networks of workstations is described. The approach, called Parallel Decision Trees (PDT), overcomes limitations of equivalent serial algorithms that have been reported by several researchers, and enables the use of the very-large-scale training sets that are increasingly of interest in real-world applications of machine learning and data mining.