NDLI: Mining functional dependencies from data

Content Provider	Springer Nature Link
Author	Yao, Hong Hamilton, Howard J.
Copyright Year	2007
Abstract	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.
Starting Page	197
Ending Page	219
Page Count	23
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	16
Issue Number	2
e-ISSN	1573756X
Language	English
Publisher	Springer US
Publisher Date	2007-09-15
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discovering functional dependencies Mining functional dependencies Implication rule Functional dependencies Data mining Knowledge discovery FD_Mine Relational databases Information Storage and Retrieval Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Statistics Artificial Intelligence (incl. Robotics) Computing Methodologies Data Mining and Knowledge Discovery
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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