On selecting interestingness measures for association rules : user oriented description and multiple criteria decision aid
Résumé
Data mining algorithms, especially those used for unsupervised learning, generate a large quantity of rules. In particular this applies to the APRIORI family of algorithms for the determination of association rules. It is hence impossible for anexpert in the field being mined to sustain these rules. To help carry out the task, many measures which evaluate the inter-estingness of rules have been developed. They make it possible to filter and sort automatically a set of rules with respect togiven goals. Since these measures may produce different results, and as experts have different understandings of what agoodrule is, we propose in this article a new direction to select thebestrules: 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 analysisand illustrates the benefit that a user, who is not a data mining expert, can achieve with such methods.
Domaines
Ordinateur et société [cs.CY]Origine | Fichiers produits par l'(les) auteur(s) |
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