Abstract
Social science is broadly defined as the analysis of human behavior whether it be at an individual or a group level. In this work, we explore the analysis of human behavior encoded as a trail of their events over time and space, which we refer to as behavioral event data. We show that such data offers challenges to data mining algorithm designers as the data to analyze is naturally multi-way, involves complex patterns that form/reform over time, and has complex interactions between groups in the population. Though the data naturally lends itself to be represented as graphs and tensors we show how existing techniques are limited in their usefulness and outline our own algorithms to overcome these challenges. In this paper, using the adversarial event behavior of blue and red forces, we show three core problems and solutions in event behavior analysis: (1) Decomposing behavior to identify areas of intense activity, (2) Predicting what groups of events are likely to occur, and (3) Analysis to identify interacting behavior given a known template.
Similar content being viewed by others
References
Aggrawal CC (2007) Data streams: models and algorithms. Springer, New York
Allison PD (1984) Event history analysis: regression for longitudinal event data. Springer, New York
Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory
Box-Steffensmeier JM (2004) Event history modeling: a guide for social scientists. Cambridge Press, Cambridge
Davidson I, Ravi SS (2005) Clustering under constraints: feasibility issues and the K-means algorithm. In: 5th SIAM data mining conference
Garey M, Johnson D, Witsenhausen H (1982) The complexity of the generalized Lloyd-Max problem. IEEE Trans Inf Theory 28: 2
Haas VJ, Caliri A (1999) Temporal duration and event size distribution at the epidemic threshold. J Biol Phys 25: 309–324
Li S, Zhu L, Zhang ZQ, Blake A (2006) Statistical learning of multi-view face detection. In: Proceedings of ECCV
Pew RW (1998) Modeling human and organizational behavior: application to military simulations. National Academic Press, Washington. ISBN: 10-0-309-06096-6
Schafer TJ (1978) The complexity of satisfiability problems. In: STOC. ACM, New York
Taskar B, Chatalbashev V, Koller D (2005) Learning structured prediction models: a large margin approach. In: Proceedings of the 22nd international conference on machine learning
Wagstaff K, Cardie C (2000) Clustering with instance-level constraints. In: Seventeenth international conference on machine learning (ICML 2000), Stanford, CA, July 2000, pp 1103–1110
Wang X, Davidson I (2010) Flexible and principled constrained spectral clustering. ACM KDD, Davis
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: Fei Wang; Hanghang Tong; Phillip Yu; Charu Aggarwal.
Rights and permissions
About this article
Cite this article
Davidson, I., Gilpin, S. & Walker, P.B. Behavioral event data and their analysis. Data Min Knowl Disc 25, 635–653 (2012). https://doi.org/10.1007/s10618-012-0269-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10618-012-0269-7