Patrick Meyer

A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package

The application of multi-attribute utility theory whose aggregation process is based on the Choquet integral requires the prior identification of a capacity. The main approaches to capacity identification proposed in the literature are reviewed and their advantages and inconveniences are discussed. All the reviewed methods have been implemented within the Kappalab R package. Their application is illustrated on a detailed example.

On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid

Abstract Data mining algorithms, especially those used for unsupervised learning, generate a large quantity of rules. In particular this applies to the A priori family of algorithms for the determination of association rules. It is hence impossible for an expert in the field being mined to sustain these rules. To help carry out the task, many measures which evaluate the interestingness of rules have been developed. They make it possible to filter and sort automatically a set of rules with respect to given goals. Since these measures may produce different results, and as experts have different understandings of what a good rule is, we propose in this article a new direction to select the best rules: a two-step solution to the problem of the recommendation of one or more user-adapted interestingness measures. First, a description of interestingness measures, based on meaningful classical properties, is given. Second, a multicriteria decision aid process is applied to this analysis and illustrates the benefit that a user, who is not a data mining expert, can achieve with such methods.

R UBIS: A bipolar-valued outranking method for the choice problem

The main concern of this article is to present the RUBIS method for tackling the choice problem in the context of multiple criteria decision aiding. Its genuine purpose is to help a decision maker to determine a single best decision alternative. Methodologically we focus on pairwise comparisons of these alternatives which lead to the concept of bipolar-valued outranking digraph. The work is centred around a set of five pragmatic principles which are required in the context of a progressive decision aiding methodology. Their thorough study and implementation in the outranking digraph lead us to define a choice recommendation as an extension of the classical digraph kernel concept.