  The concept of association rules is well--known in data mining. But often
redundancy and subsumption are not considered, and standard approaches produce
thousands or even millions of resulting association rules. Without further
information or post--mining approaches, this huge number of rules is typically
useless for the domain specialist -- which is an instance of the infamous
pattern explosion problem. In this work, we present a new definition of
redundancy and subsumption based on the confidence and the support of the rules
and propose post-- mining to prune a set of association rules. In a case study,
we apply our method to association rules mined from spatio--temporal data. The
data represent the trajectories of the ball in tennis matches -- more
precisely, the points/times the tennis ball hits the ground. The goal is to
analyze the strategies of the players and to try to improve their performance
by looking at the resulting association rules. The proposed approach is
general, and can also be applied to other spatio--temporal data with a similar
structure.
