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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Discovery of Spatio-Temporal Patterns from Foursquare by Diffusion-type Estimation and ICA
verfasst von : Yoshitatsu Matsuda, Kazunori Yamaguchi, Ken-ichiro Nishioka
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2014
Verlag: Springer International Publishing
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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.