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Using Context-Free Grammars to Constrain Apriori-based Algorithms for Mining Temporal Association Rules

Algorithms for the inference of association with sequential information have been proposed and used but are ineffective, in some cases, because too many candidate rules are extracted. Filtering the relevant ones is usually difficult and inefficient. In this work, we present an algorithm for the inference of temporal association rules that uses context-free grammars to restrict the search process, in order to filter, in an efficient and effective way, the associations discovered by the algorithm. Moreover, we present experimental results that empirically evaluate its performance using synthetic data.