MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension
published: Nov. 23, 2018, recorded: August 2018, views: 2
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
“Miso makes a soup loaded with flavour that saves you the hassle of making stock.” – Y. Ottolenghi  We present MiSoSouP, a suite of algorithms for extracting highquality approximations of the most interesting subgroups, according to different interestingness measures, from a random sample of a transactional dataset. We describe a new formulation of these measures that makes it possible to approximate them using sampling. We then discuss how pseudodimension, a key concept from statistical learning theory, relates to the sample size needed to obtain an high-quality approximation of the most interesting subgroups. We prove an upper bound on the pseudodimension of the problem at hand, which results in small sample sizes. Our evaluation on real datasets shows that MiSoSouP outperforms state-of-the-art algorithms offering the same guarantees, and it vastly speeds up the discovery of subgroups w.r.t. analyzing the whole dataset.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !