We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Mining exceptional closed patterns in attributed graphs.
- Authors
Bendimerad, Anes; Plantevit, Marc; Robardet, Céline
- Abstract
Geo-located social media provide a large amount of information describing urban areas based on user descriptions and comments. Such data make 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 suitably attributed graph model, our approach identifies neighborhoods with homogeneous and exceptional characteristics. We introduce the novel problem of exceptional subgraph mining in attributed graphs and propose a complete algorithm that takes benefits from closure operators, new upper bounds and pruning properties. We also define an approach to sample the space of closed exceptional subgraphs within a given time budget. Experiments performed on ten real datasets are reported and demonstrated the relevancy of both approaches, and also showed their limits.
- Subjects
DATA mining; SUBGRAPHS; WIRELESS geolocation systems; SOCIAL media; CITIES &; towns
- Publication
Knowledge & Information Systems, 2018, Vol 56, Issue 1, p1
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-017-1109-2