Finding fuzzy association rules via restriction levels

Association rule mining is a helpful tool to discover relations between items in transactions. But in some scenarios, it is also interesting to consider not only the presence of items, but the absence of them. In this paper, we introduce a methodology to obtain fuzzy association rules involving absent items. Additionally, our proposal is based on restriction level sets, a recent representation of fuzziness that extends that of fuzzy sets, and introduces some new operators, covering some misleading results obtained from usual fuzzy operators as, for example, negation. In our methodology, we define new measures for fuzzy association rules as RL-numbers, as well as we propose a new way of summarizing the resulting set of fuzzy association rules, distributed in restriction levels.