Paper Info
Title
A Unified View of Objective Interestingness Measures
Abstract
Association rule mining often results in an overwhelming number of rules. In practice, it is difficult for the final user to select the most relevant rules. In order to tackle this problem, various interestingness measures were proposed. Nevertheless, the choice of an appropriate measure remains a hard task and the use of several measures may lead to conflicting information. In this paper, we give a unified view of objective interestingness measures. We define a new framework embedding a large set of measures called SBMs and we prove that the SBMs have a similar behavior. Furthermore, we identify the whole collection of the rules simultaneously optimizing all the SBMs. We provide an algorithm to efficiently mine a reduced set of rules among the rules optimizing all the SBMs. Experiments on real datasets highlight the characteristics of such rules.
Year
DOI
Venue
2007
10.1007/978-3-540-73499-4_40
MLDM
Keywords
DocType
Volume
conflicting information,unified view,large set,final user,new framework,association rule mining,objective interestingness measures,hard task,appropriate measure,objective interestingness measure,various interestingness measure,overwhelming number
Conference
4571
ISSN
Citations 
PageRank 
0302-9743
13
0.62
References 
Authors
17
2
Name
Order
Citations
PageRank
Céline Hébert1181.03
Bruno Crémilleux237334.98