An enhanced relevance criterion for more concise supervised pattern discovery
Henrik Großkreutz, Daniel Paurat, Stefan Rüping · 2012
Supervised local pattern discovery aims to find subsets of a database with a high statistical unusualness in the distribution of a target attribute. Local pattern discovery is often used to generate a human-understandable representation of the most interesting dependencies in a data set. Hence, the more crisp and concise the output is, the better. Unfortunately, standard algorithm often produce very large and redundant outputs.