Abstract
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.
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References
Altman, I.: The Environment and Social Behavior: Privacy, Personal Space, Territory, and Crowding. Brooks/Cole Publishing Company, Belmont (1975)
Atzmueller, M.: Data mining on social interaction networks. JDMDH 1 (2014)
Atzmueller, M.: Subgroup discovery. WIREs DMKD 5(1), 35–49 (2015)
Atzmueller, M.: Local exceptionality detection on social interaction networks. In: Berendt, B., Bringmann, B., Fromont, É., Garriga, G., Miettinen, P., Tatti, N., Tresp, V. (eds.) ECML PKDD 2016. LNCS, vol. 9853, pp. 298–302. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46131-1_39
Atzmueller, M.: Descriptive community detection. In: Missaoui, R., Kuznetsov, S.O., Obiedkov, S. (eds.) Formal Concept Analysis of Social Networks. LNSN, pp. 41–58. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64167-6_3
Atzmueller, M.: Compositional subgroup discovery on attributed social interaction networks. In: Soldatova, L., Vanschoren, J., Papadopoulos, G., Ceci, M. (eds.) DS 2018. LNCS, vol. 11198, pp. 259–275. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01771-2_17
Atzmueller, M., Puppe, F.: SD-Map – a fast algorithm for exhaustive subgroup discovery. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS, vol. 4213, pp. 6–17. Springer, Heidelberg (2006). https://doi.org/10.1007/11871637_6
Berlanga, F., del Jesus, M.J., González, P., Herrera, F., Mesonero, M.: Multiobjective evolutionary induction of subgroup discovery fuzzy rules: a case study in marketing. In: Perner, P. (ed.) ICDM 2006. LNCS, vol. 4065, pp. 337–349. Springer, Heidelberg (2006). https://doi.org/10.1007/11790853_27
Bondy, J.A., Murty, U.S.R.: Graph Theory with Applications, vol. 290. Elsevier Science Ltd., North-Holland (1976)
Cabrera-Quiros, L., Demetriou, A., Gedik, E., van der Meij, L., Hung, H.: The matchNMingle dataset: a novel multi-sensor resource for the analysis of social interactions and group dynamics in-the-wild during free-standing conversations and speed dates. IEEE Trans. Affect. Comput. 99, 1–17 (2018)
Delener, N.: Religious contrasts in consumer decision behaviour patterns: their dimensions and marketing implications. Eur. J. Mark. 28(5), 36–53 (1994)
Duivesteijn, W., Knobbe, A.J.: Exploiting false discoveries - statistical validation of patterns and quality measures in subgroup discovery. In: ICDM, pp. 151–160. IEEE Computer Society (2011)
Gamberger, D., Lavrac, N.: Expert-guided subgroup discovery: methodology and application. J. Artif. Intell. Res. 17, 501–527 (2002)
Goffman, E. (ed.): Interaction Ritual: Essays in Face to Face Behavior. Piscataway, Aldine-Transactions (1967)
Grosskreutz, H., Rüping, S.: On subgroup discovery in numerical domains. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, p. 30. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04180-8_15
Heravi, B.M., Gibson, J.L., Hailes, S., Skuse, D.: Playground social interaction analysis using bespoke wearable sensors for tracking and motion capture. In: Proceedings of the 5th International Conference on Movement and Computing, MOCO 2018, pp. 21:1–21:8. ACM (2018)
Herrera, F., Carmona, C.J., González, P., del Jesús, M.J.: An overview on subgroup discovery: foundations and applications. Knowl. Inf. Syst. 29(3), 495–525 (2011)
Jorge, A.M., Pereira, F., Azevedo, P.J.: Visual interactive subgroup discovery with numerical properties of interest. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds.) DS 2006. LNCS, vol. 4265, pp. 301–305. Springer, Heidelberg (2006). https://doi.org/10.1007/11893318_31
Kleinberg, J.M.: Hubs, authorities, and communities. ACM Comput. Surv. 31(4es), 5 (1999)
Klösgen, W.: Handbook of Data Mining and Knowledge Discovery. Oxford University Press Inc., New York (2002)
Klösgen, W., May, M.: Spatial subgroup mining integrated in an object-relational spatial database. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS, vol. 2431, pp. 275–286. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45681-3_23
Ko, T.: A survey on behavior analysis in video surveillance for homeland security applications. In: Proceedings of the 37th IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2008, Washington, DC, USA, 15–17 October 2008, pp. 1–8 (2008)
Lauw, H.W., Lim, E., Pang, H., Tan, T.: Stevent: spatio-temporal event model for social network discovery. ACM Trans. Inf. Syst. 28(3), 15:1–15:32 (2010)
Leman, D., Feelders, A., Knobbe, A.: Exceptional model mining. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS, vol. 5212, pp. 1–16. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87481-2_1
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001)
Messinger, D.S., et al.: Continuous measurement of dynamic classroom social interactions. Int. J. Behav. Dev. 43(3), 263–270 (2019)
Newman, M.: Networks: an introduction. In: Introduction: A Short Introduction to Networks and Why We Study Them. Oxford University Press, Inc., New York (2010). Chap
Owen, N., Healy, G.N., Matthews, C.E., Dunstan, D.W.: Too much sitting: the population health science of sedentary behavior. Exerc. Sport Sci. Rev. 38(3), 182–196 (2010)
Roddick, J.F., Spiliopoulou, M.: A bibliography of temporal, spatial and spatio-temporal data mining research. SIGKDD Explor. 1(1), 34–38 (1999)
Romero, C., González, P., Ventura, S., del Jesús, M.J., Herrera, F.: Evolutionary algorithms for subgroup discovery in E-learning: a practical application using moodle data. Expert Syst. Appl. 36(2), 1632–1644 (2009)
Rebelo de Sá, C., Duivesteijn, W., Soares, C., Knobbe, A.: Exceptional preferences mining. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS, vol. 9956, pp. 3–18. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46307-0_1
Škrlj, B., Kralj, J., Vavpetič, A., Lavrač, N.: Community-based semantic subgroup discovery. In: Appice, A., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) NFMCP 2017. LNCS, vol. 10785, pp. 182–196. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78680-3_13
Terry, M.A., Mynatt, E.D., Ryall, K., Leigh, D.: Social net: using patterns of physical proximity over time to infer shared interests. In: CHI Extended Abstracts, pp. 816–817. ACM (2002)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences. Cambridge University Press, Cambridge (1994)
Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Zytkow, J. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63223-9_108
Acknowledgements
This work has been partially supported by the German Research Foundation (DFG) project “MODUS” (under grant AT 88/4-1). Furthermore, the research leading to these results has received funding (JG) from ESRC grant ES/N006577/1. This work was financed by the project Kids First, project number 68639.
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Jorge, C.C., Atzmueller, M., Heravi, B.M., Gibson, J.L., de Sá, C.R., Rossetti, R.J.F. (2019). Mining Exceptional Social Behaviour. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_38
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