Mining relaxed functional dependencies from data

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Relaxed functional dependencies (rfds) are properties expressing important relationships among data. Thanks to the introduction of approximations in data comparison and/or validity, they can capture constraints useful for several purposes, such as the identification of data inconsistencies or patterns of semantically related data. Nevertheless, rfds can provide benefits only if they can be automatically discovered from data. In this paper we present an rfd discovery algorithm relying on a lattice structured search space, previously used for fd discovery, new pruning strategies, and a new candidate rfd validation method. An experimental evaluation demonstrates the discovery performances of the proposed algorithm on real datasets, also providing a comparison with other algorithms.