  Mining association rules is an important technique for discovering meaningful
patterns in transaction databases. Many different measures of interestingness
have been proposed for association rules. However, these measures fail to take
the probabilistic properties of the mined data into account. In this paper, we
start with presenting a simple probabilistic framework for transaction data
which can be used to simulate transaction data when no associations are
present. We use such data and a real-world database from a grocery outlet to
explore the behavior of confidence and lift, two popular interest measures used
for rule mining. The results show that confidence is systematically influenced
by the frequency of the items in the left hand side of rules and that lift
performs poorly to filter random noise in transaction data. Based on the
probabilistic framework we develop two new interest measures, hyper-lift and
hyper-confidence, which can be used to filter or order mined association rules.
The new measures show significantly better performance than lift for
applications where spurious rules are problematic.
