Concise representations for approximate association rules
Yue Xu, Yuefeng Li, Gavin Shaw · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · 2008
The quality of association rule mining has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we firstly propose a definition for redundancy; then we propose a concise representation called reliable basis for representing non-redundant association rules for both exact rules and approximate rules. We prove that the redundancy elimination based on the reliable basis does not reduce the belief to the extracted rules. We also prove that all association rules can be deduced from the reliable basis. Therefore the reliable basis is a lossless representation of association rules. Experimental results show that the reliable basis significantly reduces the number of extracted rules.