Mining Exceptional Social Behaviour

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Jorge, CC (Autor) Outra; Atzmueller, M (Autor) Outra; Heravi, BM (Autor) Outra; Gibson, JL (Autor) Outra; de Sá, CR (Autor) Outra; Rosaldo J. F. Rossetti (Autor) FEUP

Ata de Conferência Internacional

Título: Progress in Artificial Intelligence

Páginas: 460-472

EPIA Conference on Artificial Intelligence

Vila Real, Setembro 2019

Indexação

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00R-4MG

DOI: 10.1007/978-3-030-30244-3_38

Abstract (EN): Essentially, our lives are made of social interactions. These can be recorded through personal gadgets as well as sensors adequately attached to people for research purposes. In particular, such sensors may record real time location of people. This location data can then be used to infer interactions, which may be translated into behavioural patterns. In this paper, we focus on the automatic discovery of exceptional social behaviour from spatio-temporal data. For that, we propose a method for Exceptional Behaviour Discovery (EBD). The proposed method combines Subgroup Discovery and Network Science techniques for finding social behaviour that deviates from the norm. In particular, it transforms movement and demographic data into attributed social interaction networks, and returns descriptive subgroups. We applied the proposed method on two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data. © 2019, Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica