I need to detect rules in a data set that contains real-valued features. A simplified example of my samples, defined by features a, b, and c, and having class 0 or 1, might look like this:
2.34, 1, 5.46 => 0
1.23, 0, 7.81 => 1
For this data set, I am interested in detecting rules like:
a > 1 and a<3 and b==1 => class=0
I have checked out association rule algorithms in Weka and then searched online, and they do what I want, but they all seem to need binary features.
My question: is there an algorithm that can do what I want, or do I need to hack my own on the basis of existing association rule algorithms?
Best Answer
It sounds like a decision tree algorithm (such as rpart in R) will do what you need. Most decision trees will find rules similar to the one you outlined above.