In this paper

aiming at the redundant rules produced during multi-level association rule mining

an approach for eliminating multi-level redundant rules based on semantic dividing is proposed. In the method

the redundancies are divided combination with semantic ontology into three categories: sub-rules redundancies

synonymous redundancies and reducible redundancies. Deleting the redundant sub-rules

grouping the synonymous rules and treating these rules having the same meaning

reducing rules that express only part of information to higher level

when pruning redundancies. Finally

the approach is evaluated on a real mobile subscription service dataset

and its effective in treatment of the redundant rules is proved through the experiment.