  Discovering episodes, frequent sets of events from a sequence has been an
active field in pattern mining. Traditionally, a level-wise approach is used to
discover all frequent episodes. While this technique is computationally
feasible it may result in a vast number of patterns, especially when low
thresholds are used.
  In this paper we propose a new quality measure for episodes. We say that an
episode is significant if the average length of its minimal windows deviates
greatly when compared to the expected length according to the independence
model. We can apply this measure as a post-pruning step to test whether the
discovered frequent episodes are truly interesting and consequently to reduce
the number of output.
  As a main contribution we introduce a technique that allows us to compute the
distribution of lengths of minimal windows using the independence model. Such a
computation task is surpisingly complex and in order to solve it we compute the
distribution iteratively starting from simple episodes and progressively moving
towards the more complex ones. In our experiments we discover candidate
episodes that have a sufficient amount of minimal windows and test each
candidate for significance. The experimental results demonstrate that our
approach finds significant episodes while ignoring uninteresting ones.
