From Visualization to Association Rules : an automatic approach

Gwenael Bothorel (1, 2) , Mathieu Serrurier (3) , Christophe Hurter (4)

1 DGAC - Direction Générale de l'Aviation Civile
2 IRIT-APO - Algorithmes Parallèles et Optimisation
3 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
4 LII - ENAC - Equipe Informatique Interactive

The main goal of Data Mining is the research of relevant information from a huge volume of data. It is generally achieved either by automatic algorithms or by the visual exploration of data. Thanks to algorithms, an exhaustive set of patterns matching specific measures can be found. But the volume of extracted information can be greater than the volume of initial data. Visual Data Mining allows the specialist to focus on a specific area of data that may describe interesting patterns. However, it is often limited by the difficulty to deal with a great number of multi dimensional data. In this paper, we propose to mix an automatic and a manual method, by driving the automatic extraction using a data scatter plot visualization. This visualization affects the number of rules found and their construction. We illustrate our method on two databases. The first describes one month French air traffic and the second stems from 2012 KDD Cup database.

Domaines: Interface homme-machine [cs.HC]; Base de données [cs.DB]

Gwenael Bothorel, Mathieu Serrurier, Christophe Hurter. From Visualization to Association Rules : an automatic approach. SCCG 2013, Spring Conference on Computer Graphics, ACM, May 2013, Smolenice, Slovakia. pp 57-64, ⟨10.1145/2508244.2508252⟩. ⟨hal-01078330⟩