Extracting non-redundant approximate rules from multi-level datasets

Abstract

Association rule mining plays an important job in knowledge and information discovery. Often the number of the discovered rules is huge and many of them are redundant, especially for multi-level datasets. Previous work has shown that the mining of non-redundant rules is a promising approach to solving this problem, with work in [14,18,19,20] focusing on single level datasets. Recent
work by Shaw et. al. [15] has extended the non-redundant
approaches presented in [14,18,19] to include the
elimination of redundant exact basis rules from multilevel
datasets. In this paper, we propose an extension to the work in [14,15,18,19,20] to allow for the removal of
hierarchically redundant approximate basis rules from
multi-level datasets through the use of the dataset’s
hierarchy or taxonomy. Experimentation shows our approach can effectively generate both multi-level and cross level non-redundant rule sets which are lossless.