My data is a set of about 1 million training examples, where each example is represented by about 2000 boolean features. Examples aren't labelled - it wouldn't really have any meaning in this domain. The features fully define the example. My goal is to learn a generative model that I can use to generate more examples. I would like to capture some of the dependencies between the features (e.g. if feature 10 is on, feature 14, 15, and 16 are usually on).

I don't have much background in ML, so I'm not sure what a simple way to do this would be (both algorithms and software packages). My initial thought was to find a structure learning package to learn a Bayesian network and then sample from it, but I figured I'd ask here to see if anyone had any recommendations.



1 Answer 1


You may try an Apriori algorithm or more advanced FPGrowth algorithm. It will give you a set of association rules, which, I think, you required:

e.g. if feature 10 is on, feature 14, 15, and 16 are usually on


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