Metarule-Guided Mining of Multi-Dimensional Association RulesUsing Data Cubes

Abstract

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. Introduction Metarule-guided mining is a interactive approach to data mining, whereby the user can probe the data under analysis by specifying hypotheses in the form of metarules, or pattern templates. A data mining system attempts to confirm the hypotheses by searching for patterns that match the given metarules. Metaruleguided mining increases the likelihood of finding rules that are of interest to the user and can make the discovery process more efficient by using the metarules...