Unsupervised Exceptional Attributed Sub-graph Mining in Urban Data

Anes Bendimerad (1) , Marc Plantevit (1) , Céline Robardet

1 DM2L - Data Mining and Machine Learning

Geo-located social media provide a wealth of information that describes urban areas based on user descriptions and comments. Such data makes possible to identify meaningful city neighborhoods on the basis of the footprints left by a large and diverse population that uses this type of media. In this paper, we present some methods to exhibit the predominant activities and their associated urban areas to automatically describe a whole city. Based on a suitable attributed graph model, our approach identifies neighborhoods with homogeneous and exceptional characteristics. We introduce the novel problem of exceptional sub-graph mining in attributed graphs and propose a complete algorithm that takes benefits from new upper bounds and pruning properties. We also propose an approach to sample the space of exceptional sub-graphs within a given time-budget. Experiments performed on 10 real datasets are reported and demonstrate the relevancy and the limits of both approaches.

Mots clés: Attributed Graph Mining; Urban Data Analysis; Exceptional Model Mining

Domaines: Base de données [cs.DB]; Intelligence artificielle [cs.AI]

Anes Bendimerad, Marc Plantevit, Céline Robardet. Unsupervised Exceptional Attributed Sub-graph Mining in Urban Data. IEEE International Conference on Data Mining (ICDM 2016), Dec 2016, Barcelone, Spain. pp.21-30. ⟨hal-01430622⟩