Standard decision tree algorithms, such as ID3 and C4.5, have a brute force approach for choosing the cut point in a continuous feature. Every single value is tested as a possible cut point. (By tested I mean that e.g. the Information gain is calculated at every possible value.)
With many continuous features and a lot of data (hence many values for each feature) this apporach seems very inefficient!
I'm assuming finding a better way to do this is a hot topic in Machine Learning. In fact my Google Scholar search revealed some alternative approaches. Such as discretizing with k-means. Then there seem to be a lot of papers that tackle specific problems in specific domains.
But is there a recent review paper, blog post or book that gives an overview on common apporaches for discretization? I couldn't find one...
Or else, maybe one of you is an expert on the topic and willing to write up a small overview. That would be tremendously helpful!