You can train decision trees much faster by pre-selecting promising split candidates using data clustering, without sacrificing accuracy—useful when working with large datasets.
This paper proposes DICS, a method to speed up decision tree training by using clustering to generate a smaller set of candidate splits instead of searching all possible splits. The approach maintains accuracy while reducing computation time, and works with random forests and gradient boosting too.