Discovery of Spatio-Temporal Patterns from Foursquare by Diffusion-type Estimation and ICA
Discovery of Spatio-Temporal Patterns from Foursquare by Diffusion-type Estimation and ICA
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DOI：
10.1007/978-3-319-11179-7_96
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发表时间：
2014-03
期刊：
影响因子：
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通讯作者：
Yoshitatsu Matsuda;K. Yamaguchi;Ken-ichiro Nishioka
中科院分区：
文献类型：
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作者：
Yoshitatsu Matsuda;K. Yamaguchi;Ken-ichiro Nishioka
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.
