Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes

Micheline Kamber, Jiawei Han, Jenny Y. Chiang

In this paper, we employ a novel approach to metarule-guided, multi-dimensional association rule mining which explores a data cube structure. We propose algorithms for metarule-guided mining: given a metarule containing p predicates, we compare mining on an n-dimensional (n-D) cube structure (where p < n) with mining on smaller multiple p-dimensional cubes. In addition, we propose an efficient method for precomputing the cube, which takes into account the constraints imposed by the given metarule.

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