KDD Papers

Discovering Reliable Approximate Functional Dependencies

Panagiotis Mandros (Max Planck Institute for Informatics);Mario Boley (Max Planck Institute for Informatics);Jilles Vreeken (Max Planck Institute for Informatics)


Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependency of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependency? And, how can we efficiently discover the optimal or alpha-approximate top-k dependencies? These are exactly the questions we answer in this paper.

As we want to be agnostic on the form of the dependency, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity.