Mining Association Rules in OLAP Cubes

On-line analytical processing (OLAP) provides tools to explore data cubes in order to extract interesting information. Nevertheless, OLAP is not capable of explaining relationships that could exist within data. Association rules are one kind of data mining techniques which finds associations among data. In this paper, we propose a framework for mining association rules from data cubes according to a sum-based aggregate measure which is more general than frequencies provided by the 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 algorithm for mining inter-dimensional association rules directly from a multidimensional structure of data