Paper Info
Title
Formal and computational properties of the confidence boost of association rules
Abstract
Some existing notions of redundancy among association rules allow for a logical-style characterization and lead to irredundant bases of absolutely minimum size. We push the intuition of redundancy further to find an intuitive notion of novelty of an association rule, with respect to other rules. Namely, an irredundant rule is so because its confidence is higher than what the rest of the rules would suggest; then, one can ask: how much higher? We propose to measure such a sort of novelty through the confidence boost of a rule. Acting as a complement to confidence and support, the confidence boost helps to obtain small and crisp sets of mined association rules and solves the well-known problem that, in certain cases, rules of negative correlation may pass the confidence bound. We analyze the properties of two versions of the notion of confidence boost, one of them a natural generalization of the other. We develop algorithms to filter rules according to their confidence boost, compare the concept to some similar notions in the literature, and describe the results of some experimentation employing the new notions on standard benchmark datasets. We describe an open source association mining tool that embodies one of our variants of confidence boost in such a way that the data mining process does not require the user to select any value for any parameter.
Year
DOI
Venue
2011
10.1145/2541268.2541272
ACM Transactions on Knowledge Discovery from Data (TKDD)
Keywords
DocType
Volume
similar notion,irredundant rule,confidence boost,intuitive notion,existing notion,data mining process,open source association mining,mined association rule,association rule,new notion,computational property
Journal
7
Issue
ISSN
Citations 
4
1556-4681
5
PageRank 
References 
Authors
0.43
35
1
Name
Order
Citations
PageRank
José L. Balcázar170162.06