Rui Li and Stefan Kramer (2012)
Efficient Redundancy Reduced Subgroup Discovery via Quadratic Programming
In: The 15th International Conference on Discovery Science, pp. 125–138, Lyon, France, Springer-Verlag. Lecture Notes in Artificial Intelligence (LNAI).
Subgroup discovery is a task at the intersection of predictive
and descriptive induction, aiming at identifying subgroups that have the
most unusual statistical (distributional) characteristics with respect to a
property of interest. Although a great deal of work has been devoted to
the topic, one remaining problem concerns the redundancy of subgroup
descriptions, which often effectively convey very similar information. In
this paper, we propose a quadratic programming based approach to reduce
the amount of redundancy in the subgroup rules. Experimental
results on 12 datasets show that the resulting subgroups are in fact less
redundant compared to standard methods. In addition, our experiments
show that the computational costs are significantly lower than the one
of other methods compared in the paper.
