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

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 pdimensional cubes. In addition, we propose an efficient method for precomputing the cube, which takes into account the constraints imposed by the given metarule.

• Type: Conference
• Year: 1997
• Where: KDD
• Authors: Micheline Kamber, Jiawei Han, Jenny Chiang
• Keywords: Association Rule Mining, Cube Structure, Data Cube Structure, Data Mining, KDD 1997