Mining Multidimensional Association Rules

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Rakesh Sharma
Pinki Sharma


Association rule mining plays an important role in knowledge and information discovery. However, existing association rule mining is only focused on single level datasets. In this paper, we firstly present a introduction for multidimensional association rules, then we present introduction about static discretization approach for mining multidimensional association rule In previous studies the association rules are generated as single dimension however mining association rules at multiple dimension may lead to the discovery of more specific and concrete knowledge from large transaction databases by extension of some existing rules mining techniques. In multidimensional association rules we use same minimum support for different conceptual levels. In this paper, we also discover multidimensional (cross–level) association rules using MLT2 algorithm detailed in Han and Fu’s paper. This algorithm discovers association rules for successive levels making use of rules already discovered for cross levels of concept hierarchy.


Keywords: Data mining, support, Association rules, Multidimensional Association rule.


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