TY - GEN
T1 - Mining Exceptional Social Behaviour
AU - Centeio Jorge, Carolina
AU - Atzmueller, Martin
AU - Heravi, Behzad M.
AU - Gibson, Jenny L.
AU - de Sá, Cláudio Rebelo
AU - Rossetti, Rosaldo J.F.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Network science
KW - Social interactions
KW - Subgroup discovery
UR - http://www.scopus.com/inward/record.url?scp=85072860994&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30244-3_38
DO - 10.1007/978-3-030-30244-3_38
M3 - Conference contribution
AN - SCOPUS:85072860994
SN - 9783030302436
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 460
EP - 472
BT - Progress in Artificial Intelligence - 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Proceedings
A2 - Moura Oliveira, Paulo
A2 - Novais, Paulo
A2 - Reis, Luís Paulo
PB - Springer
T2 - 19th EPIA Conference on Artificial Intelligence, EPIA 2019
Y2 - 3 September 2019 through 6 September 2019
ER -