It's a contradiction---no, it's not: a case study using functional relations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The VLDB Journal — The International Journal on Very Large Data Bases
Identifying functional relations in web text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Reverse engineering user interfaces for interactive database conceptual analysis
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
On attribute reduction of rough set based on pruning rules
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Interactively eliciting database constraints and dependencies
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Database semantics recovery through analysis of dynamic SQL statements
Journal on data semantics XV
Functional dependency discovery via Bayes net analysis
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Effects of data set features on the performances of classification algorithms
Expert Systems with Applications: An International Journal
Mining frequent conjunctive queries using functional and inclusion dependencies
The VLDB Journal — The International Journal on Very Large Data Bases
An extended synthesis algorithm for relational database schema design
Proceedings of the 2013 International Conference on Information Systems and Design of Communication
ACM SIGMOD Record
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In this paper, we propose an efficient rule discovery algorithm, called FD_Mine, for mining functional dependencies from data. By exploiting Armstrong's Axioms for functional dependencies, we identify equivalences among attributes, which can be used to reduce both the size of the dataset and the number of functional dependencies to be checked. We first describe four effective pruning rules that reduce the size of the search space. In particular, the number of functional dependencies to be checked is reduced by skipping the search for FDs that are logically implied by already discovered FDs. Then, we present the FD_Mine algorithm, which incorporates the four pruning rules into the mining process. We prove the correctness of FD_Mine, that is, we show that the pruning does not lead to the loss of useful information. We report the results of a series of experiments. These experiments show that the proposed algorithm is effective on 15 UCI datasets and synthetic data.