  In many applications it will be useful to know those patterns that occur with
a balanced interval, e.g., a certain combination of phone numbers are called
almost every Friday or a group of products are sold a lot on Tuesday and
Thursday.
  In previous work we proposed a new measure of support (the number of
occurrences of a pattern in a dataset), where we count the number of times a
pattern occurs (nearly) in the middle between two other occurrences. If the
number of non-occurrences between two occurrences of a pattern stays almost the
same then we call the pattern balanced.
  It was noticed that some very frequent patterns obviously also occur with a
balanced interval, meaning in every transaction. However more interesting
patterns might occur, e.g., every three transactions. Here we discuss a
solution using standard deviation and average. Furthermore we propose a simpler
approach for pruning patterns with a balanced interval, making estimating the
pruning threshold more intuitive.
