Word association norms, mutual information, and lexicography
Computational Linguistics
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Domination of aggregation operators and preservation of transitivity
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fuzzy orderings in flexible query answering systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Compression-Based Measures for Mining Interesting Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
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One of the concerns of knowledge discovery in databases is the production of association rules. An association rule A$\longrightarrow$B defines a relationship between two sets of attributes A and B, caracterising the data studied. Such a rule means that objects sharing attributes of A will “likely” have those contained in B. Yet, this notion of “likeliness” depends on the datamining context. Many interestingness measures have been introduced in order to quantify this likeliness. This panel of measures is heterogeneous and the ranking of extracted rules, according to measures, may differ largely. This contribution explores a new approach for assessing the quality of rules: aggregating valued relations. For each measure, a valued relation is built out of the numerical values it takes on the rules, and represents the preference of a rule over another. The aim in using such tools is to take into account the intensity of preference expressed by various measures, and should reduce incomparability issues related to differences in their co-domains. It also has the advantage of relating the numerical nature of measures compared to pure binary approaches. We studied several aggregation operators. In this contribution we discuss results obtained on a toy example using the simplest of them.