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2006
ACM

Enhanced mining of association rules from data cubes

11 years 11 months ago
Enhanced mining of association rules from data cubes
On-line analytical processing (OLAP) provides tools to explore and navigate into data cubes in order to extract interesting information. Nevertheless, OLAP is not capable of explaining relationships that could exist in a data cube. Association rules are one kind of data mining techniques which finds associations among data. In this paper, we propose a framework for mining inter-dimensional association rules from data cubes according to a sum-based aggregate measure more general than simple frequencies provided by the traditional COUNT measure. Our mining process is guided by a meta-rule context driven by analysis objectives and exploits aggregate measures to revisit the definition of support and confidence. We also evaluate the interestingness of mined association rules according to Lift and Loevinger criteria and propose an efficient algorithm for mining inter-dimensional association rules directly from a multidimensional data. Categories and Subject Descriptors: H.2.8 [Informatio...
Riadh Ben Messaoud, Sabine Loudcher Rabaséd
Added 13 Jun 2010
Updated 13 Jun 2010
Type Conference
Year 2006
Where DOLAP
Authors Riadh Ben Messaoud, Sabine Loudcher Rabaséda, Omar Boussaid, Rokia Missaoui
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