Abstract
Coupling social media with geographic location has boosted the worth of understanding the real-world situations. In particular, event detection based on clustering algorithms or bursty detection aims to find more specific topics that represent real-world events from geo-tagged social media. However, it is also necessary to identify unusual and seemingly inconsistent patterns in data, namely outliers. For example, it is difficult to obtain social media posted by residents of the places where a disaster is happening for quite some while. In this paper, we focus on a problem in partitioning a space to find a meaningful local outlier pattern by using a genetic algorithm (GA). We first describe a model of local patterns based on spatio-temporal neighbors and a normal distribution test. Then we propose our optimization process to maximize the number of patterns. Finally, we show results of the performance simulation with a real dataset related to a landslide disaster.
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Acknowledgments
This work was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant No. 15K15995, and based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
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Kumrai, T., Kim, KS., Dong, M., Ogawa, H. (2017). Optimal KD-Partitioning for the Local Outlier Detection in Geo-Social Points. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_13
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DOI: https://doi.org/10.1007/978-3-319-59072-1_13
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