Skip to Main content Skip to Navigation
New interface
Conference papers

Unsupervised Exceptional Attributed Sub-graph Mining in Urban Data

Anes Bendimerad 1 Marc Plantevit 1 Céline Robardet 
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : 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.
Complete list of metadata

Cited literature [22 references]  Display  Hide  Download
Contributor : Anes Bendimerad Connect in order to contact the contributor
Submitted on : Tuesday, January 10, 2017 - 10:10:50 AM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM
Long-term archiving on: : Tuesday, April 11, 2017 - 1:55:15 PM


Exceptional subgraph mining.pd...
Files produced by the author(s)


  • HAL Id : hal-01430622, version 1


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⟩



Record views


Files downloads