Abstract
The discovery of differential dependencies (DDs) is the problem of finding a minimal cover set of DDs that hold in a given relation. This paper proposes a novel subspace-clustering-based approach to mine DDs that exist in a given relation. We study and reveal a link between δ-nClusters and differential functions (DFs). Based on this relationship, we adopt and co-opt techniques for mining δ-nClusters to find the set of candidate antecedent DFs of DDs efficiently, based on a user-specified distance threshold. Furthermore, we define an interestingness measure for DDs to aid the discovery of essential DDs and avoid the mining of an extremely large set. Finally, we demonstrate the scalability and efficiency of our solution through experiments on real-world benchmark datasets.
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Kwashie, S., Liu, J., Li, J., Ye, F. (2014). Mining Differential Dependencies: A Subspace Clustering Approach. In: Wang, H., Sharaf, M.A. (eds) Databases Theory and Applications. ADC 2014. Lecture Notes in Computer Science, vol 8506. Springer, Cham. https://doi.org/10.1007/978-3-319-08608-8_5
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DOI: https://doi.org/10.1007/978-3-319-08608-8_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08607-1
Online ISBN: 978-3-319-08608-8
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