Abstract
This study develops the foundation for a simple, yet efficient method for uncovering functional and approximate functional dependencies in relational databases. The technique is based upon the mathematical theory of partitions defined over a relation's row identifiers. Using a levelwise algorithm the minimal non-trivial functional dependencies can be found using computations conducted on integers. Therefore, the required operations on partitions are both simple and fast. Additionally, the row identifiers provide the added advantage of nominally identifying the exceptions to approximate functional dependencies, which can be used effectively in practical data mining applications.
Highlights
The complexity of discovering functional dependencies has been studied in [5], [6], [7]
A functional dependency states that the value of an attribute is uniquely determined by the value of some other attributes
Suppose that a company sets up a database to keep track of its employees and the various departments to which they are assigned from time to time
Summary
The complexity of discovering functional dependencies has been studied in [5], [6], [7]. Note that First name plus Family name determining Salary is an approximate functional dependency with a 25 percent error rate. The algorithm for discovering functional and approximate functional dependencies employed in this paper is similar to the levelwise approach for the discovery of association rules [1]. This search strategy first computes some non–trivial information about attribute sets, frequent item sets, and which association rules can be computed . Using rough sets [12] utilized rough sets for identifying the most critical factors for allowing for the elimination of irrelevant attributes in a relation prior to the generation of rules describing data dependencies in databases
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