Extracting Non-redundant Approximate Rules from Multi-level Datasets
Gavin Shaw, Yue Xu, Shlomo Geva · 2008
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 focusing on single level datasets. Recent work by Shaw et. al. has extended the non-redundant approaches presented in to include the elimination of redundant exact basis rules from multi-level datasets. In this paper, we propose an extension to the work in to allow for the removal of hierarchically redundant approximate basis rules from multi-level datasets through the use of the datasetpsilas hierarchy or taxonomy. Experimentation shows our approach can effectively generate both multi-level and cross level non-redundant rule sets which are lossless.