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Title
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Revisiting conditional functional dependency discovery : splitting the “C” from the “FD”
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Author
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Abstract
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| any techniques for cleaning dirty data are based on enforcing some set of integrity constraints. Conditional functional dependencies (CFDs) are a combination of traditional Functional dependencies (FDs) and association rules, and are widely used as a constraint formalism for data cleaning. However, the discovery of such CFDs has received limited attention. In this paper, we regard CFDs as an extension of association rules, and present three general methodologies for (approximate) CFD discovery, each using a different way of combining pattern mining for discovering the conditions (the “C” in CFD) with FD discovery. We discuss how existing algorithms fit into these three methodologies, and introduce new techniques to improve the discovery process. We show that the right choice of methodology improves performance over the traditional CFD discovery method CTane. |
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Language
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English
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Source (journal)
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Lecture notes in computer science. - Berlin, 1973, currens
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Source (book)
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Machine learning and knowledge discovery in databases : European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), September 10-14, 2018, Dublin, Ireland
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Source (series)
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Lecture notes in artificial intelligence (LNAI); 11052
Lecture notes in computer science (LNCS); 11052
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Publication
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Cham
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Springer
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2019
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ISBN
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978-3-030-10927-1
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978-3-030-10927-1
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DOI
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10.1007/978-3-030-10928-8_33
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Volume/pages
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(2019)
, p. 552-568
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ISI
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000611394900033
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Full text (Publisher's DOI)
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Full text (open access)
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