On pruning strategies for discovery of generalized and quantitative association rules

Mining association rules has become an important datamining task, and meanwhile many algorithms have been developed which often differ in several aspects. In this paper, we analyse and compare the pruning strategies of several algorithms that were designed for mining generalised and quantitative association rules while abstracting from other technical details. Furthermore, we sketch a novel pruning strategy Genex that exploits all information provided by a taxonomy for pruning and is applicable with a \horizontal" database layout. In the context of mining quantitative association rules, we suggest a novel representation for intervals in terms of interval boundaries.