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Discovery of Regular and Irregular SpatioTemporal Patterns from LocationBased SNS by DiffusionType Estimation
Yoshitatsu MATSUDA Kazunori YAMAGUCHI Kenichiro NISHIOKA
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E98D
No.9
pp.16751682 Publication Date: 2015/09/01 Publicized: 2015/06/10 Online ISSN: 17451361
DOI: 10.1587/transinf.2015EDP7095 Type of Manuscript: PAPER Category: Artificial Intelligence, Data Mining Keyword: data mining, SNS, spatiotemporal pattern, diffusiontype formula, Bayesian estimation, principal component analysis,
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Summary:
In this paper, a new approach is proposed for extracting the spatiotemporal patterns from a locationbased social networking system (SNS) such as Foursquare. The proposed approach consists of the following procedures. First, the spatiotemporal behaviors of users in SNS are approximated as a probabilistic distribution by using a diffusiontype formula. Since the SNS datasets generally consist of sparse checkin's of users at some time points and locations, it is difficult to investigate the spatiotemporal patterns on a wide range of time and space scales. The proposed method can estimate such wide range patterns by smoothing the sparse datasets by a diffusiontype formula. It is crucial in this method to estimate robustly the scale parameter by giving a prior generative model on checkin's of users. The robust estimation enables the method to extract appropriate patterns even in small local areas. Next, the covariance matrix among the time points is calculated from the estimated distribution. Then, the principal eigenfunctions are approximately extracted as the spatiotemporal patterns by principal component analysis (PCA). The distribution is a mixture of various patterns, some of which are regular ones with a periodic cycle and some of which are irregular ones corresponding to transient events. Though it is generally difficult to separate such complicated mixtures, the experiments on an actual Foursquare dataset showed that the proposed method can extract many plausible and interesting spatiotemporal patterns.

