Towards Association Rules as a Predictive Tool for Geospatial Areas Evolution

Asma Gharbi (1, 2) , Cyril de Runz (3, 4, 1) , Sami Faiz (5) , Herman Akdag (1)

1 LIASD - Laboratoire d'Informatique Avancée de Saint-Denis
2 ltsirs
3 LFI - Learning, Fuzzy and Intelligent systems
4 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804
5 UMA - Université de la Manouba [Tunisie]

Although it was basically presented as an exploratory tool rather than a predictive tool, numerous follow-up researchers have enhanced association rule mining, which contributes in making it a powerful predictive tool. In this context, this paper review the main advances in this datamining technique, then attempts to describe how they can, practically, be harnessed to deal with problems such as the prediction of geographical areas evolution.

Mots clés: Class Association Rules; Sequential Association Rules; Association Rules; Prediction; Spatial Dynamics

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Géographie

Asma Gharbi, Cyril de Runz, Sami Faiz, Herman Akdag. Towards Association Rules as a Predictive Tool for Geospatial Areas Evolution. International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM), Apr 2016, Rome, Italy. pp.201-206, ⟨10.5220/0005914202010206⟩. ⟨hal-01340761⟩