Ranking and selecting association rules based on dominance relationship

The huge number of association rules represent the main obstacle that a decision maker faces. In order to bypass this obstacle, an efficient selection of rules must be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures caused a new problem which is the heterogeneity of the evaluation results and this created confusion to the decision. In this scope, we propose a novel approach to discover interesting association rules without favouring or excluding any measure by adopting the notion of dominance between rules. Our approach bypasses the problem of measure heterogeneity and find a compromise between their evaluations and also bypasses another non-trivial problem which is the threshold value specification

Towards a semantic and statistical selection of association rules

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. Association rules selection is a classical topic to address this issue, yet, new innovated approaches are required in order to provide help to decision makers. Hence, many interesting- ness measures have been defined to statistically evaluate and filter the association rules. However, these measures present two major problems. On the one hand, they do not allow eliminating irrelevant rules, on the other hand, their abun- dance leads to the heterogeneity of the evaluation results which leads to confusion in decision making. In this paper, we propose a two-winged approach to select statistically in- teresting and semantically incomparable rules. Our statis- tical selection helps discovering interesting association rules without favoring or excluding any measure. The semantic comparability helps to decide if the considered association rules are semantically related i.e comparable. The outcomes of our experiments on real datasets show promising results in terms of reduction in the number of rules

Contribution to the extraction of association rules based on preferences

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Contribution à l'extraction des règles d'association basée sur des préférences

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Ranking and selecting association rules based on dominance relationship

Abstract. The huge number of association rules represent the main obstacle that a decision maker faces. In order to bypass this obstacle, an efficient selection of rules must be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures caused a new problem which is the heterogeneity of the evaluation results and this created confusion to the decision. In this scope, we propose a novel approach to discover interesting association rules without favouring or excluding any measure by adopting the notion of dominance between rules. Our approach bypasses the problem of measure heterogeneity and find a compromise between their evaluations and also bypasses another non-trivial problem which is the threshold value specification.