Closed Sets for Labeled Data
Gemma Casas-Garriga, Petra Kralj and Nada Lavrac
Closed sets are being successfully applied in the context of
compacted data representation for association rule learning.
However, their use is mainly descriptive. This paper shows
that, when considering labeled data, closed sets
can be adapted for prediction and discrimination purposes by conveniently
contrasting covering properties on positive and negative
examples. We formally justify that these sets characterize
the space of relevant combinations of features for discriminating
the target class. In practice, identifying relevant/irrelevant
combinations of features through closed sets is useful
in many applications. Here we apply it to compacting emerging patterns and
essential rules and to learn descriptions for subgroup discovery.