A Novel Incremental Approach to Association Rule Mining in Inductive Databases

Constraints-based mining languages are widely exploited to enhance the KDD process. In this paper we propose a novel incremental approach to extract itemsets and association rules from large databases. Here incremental is used to emphasize that the mining engine does not start from scratch. Instead, it exploits the result set of previously executed queries in order to simplify the mining process. Incremental algorithms show several beneficial features. First of all they exploit previous results in the pruning of the itemset lattice. Second, they are able to exploit the mining constraints of the current query in order to prune the search space even more. In this paper we propose two incremental algorithms that are able to deal with two — recently identified — types of constraints, namely item dependent and context dependent ones. Moreover, we describe an algorithm that can be used to extract association rules from scratch in presence of context dependent constraints.