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 delta-nClusters and differential functions (DFs). Based on this relationship, we adopt and co-opt techniques for mining delta-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.
Original language | English |
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Title of host publication | Databases Theory And Applications, ADC 2014 |
Editors | Hua Wang, Mohamed A. Sharaf |
Place of Publication | Berlin, Germany |
Publisher | Springer-Verlag Berlin Heidelberg |
Pages | 50-61 |
Number of pages | 12 |
Volume | 8506 |
Edition | 1st |
ISBN (Electronic) | 9783319086071 |
ISBN (Print) | 9783319086088 |
Publication status | Published - 2014 |
Event | Australasian Database Conference: ADC 2014 - University of Queensland, Brisbane, Australia Duration: 14 Jul 2014 → 16 Jul 2014 |
Conference
Conference | Australasian Database Conference |
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Country/Territory | Australia |
City | Brisbane |
Period | 14/07/14 → 16/07/14 |