"Want to come play with me?" Outlier subgroup discovery on spatio-temporal interactions

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Carolina Centeio Jorge (Author) Other; Martin Atzmueller (Author) Other; Behzad M. Heravi (Author) Other; Jenny L. Gibson (Author) Other; Rosaldo J. F. Rossetti (Author) FEUP; Cláudio Rebelo de Sá (Author) Other

Journal

Title: Expert Systems

Vol. 40 No. 5

Pages: 1-20

ISSN: 0266-4720

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 1 Citation

ISI Web of Science

Scopus - 1 Citation

Scientific classification

FOS: Natural sciences > Mathematics

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-00T-MFT

DOI: 10.1111/exsy.12686

Abstract (EN): Our lives are made of social interactions which can be recorded through personal gadgets as well as sensors capturing ubiquitous and social data. This type of data, such as spatio-temporal data from the real-time location of people, for example, can then be used for inferring interactions which can be translated into behavioural patterns. In this paper, we consider the automatic discovery of exceptional social behaviour from spatio-temporal interaction data, focusing on two areas: exceptional subgroups and spatio-temporal outliers - both in the form of descriptive patterns. For that, we propose a method for exceptional social behaviour discovery, combining subgroup discovery and network science methods for identifying behaviour that deviates from the norm. We also propose the use of two outlier detection metrics for identifying outliers, namely the Local Outlier Factor (LOF) and the Voronoi area. We applied the proposed method on synthetic data as well as 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20