Paper Info
Title
Using association rules to mine for strong approximate dependencies
Abstract
In this paper we deal with the problem of mining for approximate dependencies (AD) in relational databases. We introduce a definition of AD based on the concept of association rule, by means of suitable definitions of the concepts of item and transaction. This definition allow us to measure both the accuracy and support of an AD. We provide an interpretation of the new measures based on the complexity of the theory (set of rules) that describes the dependence, and we employ this interpretation to compare the new measures with existing ones. A methodology to adapt existing association rule mining algorithms to the task of discovering ADs is introduced. The adapted algorithms obtain the set of ADs that hold in a relation with accuracy and support greater than user-defined thresholds. The experiments we have performed show that our approach performs reasonably well over large databases with real-world data.
Year
DOI
Venue
2008
10.1007/s10618-008-0092-3
Data Min. Knowl. Discov.
Keywords
DocType
Volume
Approximate dependencies,Association rules,Data mining,Relational databases
Journal
16
Issue
ISSN
Citations 
3
1384-5810
8
PageRank 
References 
Authors
0.52
28
5
Name
Order
Citations
PageRank
Daniel Sánchez196760.29
José María Serrano280.52
Ignacio J. Blanco3868.04
Maria J. Martín-Bautista420823.79
María Amparo Vila Miranda5118293.57