A Relational Approach for Discovering Frequent Patterns with Disjunctions

Abstract

Traditional pattern discovery approaches permit to identify frequent patterns expressed in form of conjunctions of items and represent their frequent co-occurrences. Although such approaches have been proved to be effective in descriptive knowledge discovery tasks, they can miss interesting combinations of items which do not necessarily occur together. To avoid this limitation, we propose a method for discovering interesting patterns that consider disjunctions of items that, otherwise, would be pruned in the search. The method works in the relational data mining setting and conserves anti-monotonicity properties that permit to prune the search. Disjunctions are obtained by joining relations which can simultaneously or alternatively occur, namely relations deemed similar in the applicative domain. Experiments and comparisons prove the viability of the proposed approach.