I have a dataset which consists of two classes. Each record in that dataset is described by a bitvector. I split the dataset to learn and test set both containing items from two classes.
Now I would like to create classification model using Association rules nodes but I'm confused a little bit. Could anyone give me some advice how to do it?
I guess, you can use the normal Association Rule Learner but need to match the sets afterwards with the test data. Please check out the Subset Matcher node which allows finding subsets of cells within a collection. However, you need to translate your bitvector into a collection first - if somehow possible?
I guess, you need to lower the support (in%) in the node configuration, that is, the minimum number of items that appear frequent within all transactions...