Abstract
We propose efficient and precise discoveries of approximate Conditional Functional Dependencies (CFDs), by providing a precise formalization of approximate CFDs and presenting three discovery algorithms approxCFDMiner, approxCTANE and approxFastCFD as extensions of existing algorithms with renewed techniques. First, approxCFDMiner introduces a global FP-tree traversal for finding Right-hand Side items. Second, approxCTANE uses a modified pruning strategy. Third, approxFastCFD adopts a minimal coverset that is used to exclude non-minimal approximate CFDs. For these algorithms, we theoretically proved the correctness and experimentally evaluated the performances.
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Nakayama, H., Hoshino, A., Ito, C., Kanno, K. (2013). Formalization and Discovery of Approximate Conditional Functional Dependencies. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40285-2_12
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DOI: https://doi.org/10.1007/978-3-642-40285-2_12
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