Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes
نویسندگان
چکیده
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.
منابع مشابه
Using Data Cubes for Metarule-Guided Mining of Multi-Dimensional Association Rules
Metarule-guided mining is an interactive approach to data mining, where users probe the data under analysis by specifying hypotheses in the form of metarules, or pattern templates. Previous methods for metarule-guided mining of association rules have primarily used a transac-tion/relation table-based structure. Such approaches require costly, multiple scans of the data in order to nd all the la...
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