  One of the biggest setbacks in traditional frequent pattern mining is that
overwhelmingly many of the discovered patterns are redundant. A prototypical
example of such redundancy is a freerider pattern where the pattern contains a
true pattern and some additional noise events. A technique for filtering
freerider patterns that has proved to be efficient in ranking itemsets is to
use a partition model where a pattern is divided into two subpatterns and the
observed support is compared to the expected support under the assumption that
these two subpatterns occur independently.
  In this paper we develop a partition model for episodes, patterns discovered
from sequential data. An episode is essentially a set of events, with possible
restrictions on the order of events. Unlike with itemset mining, computing the
expected support of an episode requires surprisingly sophisticated methods. In
order to construct the model, we partition the episode into two subepisodes. We
then model how likely the events in each subepisode occur close to each other.
If this probability is high---which is often the case if the subepisode has a
high support---then we can expect that when one event from a subepisode occurs,
then the remaining events occur also close by. This approach increases the
expected support of the episode, and if this increase explains the observed
support, then we can deem the episode uninteresting. We demonstrate in our
experiments that using the partition model can effectively and efficiently
reduce the redundancy in episodes.
