Abstract
In this paper, we extract various patterns of the spatio-temporal distribution from Foursquare. Foursquare is a location-based social networking system which has been widely used recently. For extracting patterns, we employ ICA (Independent Component Analysis), which is a useful method in signal processing and feature extraction. Because the Foursquare dataset consists of check-in’s of users at some time points and locations, ICA is not directly applicable to it. In order to smooth the dataset, we estimate a continuous spatio-temporal distribution by employing a diffusion-type formula. The experiments on an actual Foursquare dataset showed that the proposed method could extract some plausible and interesting spatio-temporal patterns.
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References
Basak, J., Sudarshan, A., Trivedi, D., Santhanam, M.S.: Weather data mining using independent component analysis. J. Mach. Learn. Res. 5, 239–253 (2004)
Cheng, Z., Caverlee, J., Kamath, K.Y., Lee, K.: Toward traffic-driven location-based web search. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 805–814. ACM, New York (2011)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. Wiley (2002)
Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis. Journal of Neuroscience Methods 134(1), 9–21 (2004)
Eagle, N., Pentland, A.S.: Eigenbehaviors: identifying structure in routine. Behavioral Ecology and Sociobiology 63(11), 1057–1066 (2009)
Feynman, R., Hibbs, A.: Quantum mechanics and path integrals. International series in pure and applied physics, McGraw-Hill (1965)
Fire, M., Kagan, D., Puzis, R., Rokach, L., Elovici, Y.: Data mining opportunities in geosocial networks for improving road safety. In: 2012 IEEE 27th Convention of Electrical Electronics Engineers in Israel (IEEEI), pp. 1–4 (2012)
Huang, C.M., Jia-Chin Ying, J., Tseng, V.: Mining users behavior and environment for semantic place prediction. In: Nokia Mobile Data Challenge 2012 Workshop. Dedicated task (2012)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley (2001)
McKeown, M.J., Makeig, S., Brown, G., Jung, T.P., Kindermann, S.S., Bell, A., Sejnowski, T.: Analysis of fMRI data by decomposition into independent spatial components. Human Brain Mapping 6(3), 160–188 (1998)
Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: An empirical study of geographic user activity patterns in foursquare. In: ICWSM 2011, pp. 70–573 (2011)
Rae, A., Murdock, V., Popescu, A., Bouchard, H.: Mining the web for points of interest. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 711–720. ACM, New York (2012)
Schoenberg, F.P., Brillinger, D.R., Guttorp, P.: Point Processes, Spatial-Temporal, pp. 1573–1577. John Wiley & Sons, Ltd. (2006)
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Matsuda, Y., Yamaguchi, K., Nishioka, Ki. (2014). Discovery of Spatio-Temporal Patterns from Foursquare by Diffusion-type Estimation and ICA. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_96
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DOI: https://doi.org/10.1007/978-3-319-11179-7_96
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