An efficient Bayesian network approach for discovering interesting patterns

The main problem faced by all association rule/pattern mining algorithms is their production of a large number of rules which incurred a secondary mining problem; namely, mining interesting association rules/patterns. The problem is compounded by the fact that ‘common knowledge’ discovered rules are not interesting, but they are usually strong rules with high support and confidence levels – the classical measures. In this paper, we present an efficient algorithm for discovering interesting (unexpected) patterns based on background knowledge, represented by a Bayesian network. A pattern/rule is unexpected if it is ‘surprising’ to the user. The algorithm profiles a pattern as interesting (unexpected), if the absolute difference between its support estimated from the dataset and the Bayesian network exceeds a user specified threshold (ε). Itemsets with the highest diverging supports are considered the most interesting. The efficiency of the Java implementation of the algorithm is verified experimentally.