Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling
| Autor: | Xin Du, Yulong Pei, Mykola Pechenizkiy, Wouter Duivesteijn |
|---|---|
| Přispěvatelé: | Data Mining, EAISI Health, EAISI Foundational |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Process (engineering) Computer science collective social media Bayesian probability Posterior probability Exceptional model mining Machine learning computer.software_genre Task (project management) Collective social media Spatio-temporal analytics subgroup discovery Urban planning Selection (linguistics) Social media business.industry exceptional model mining Behavioral pattern Computer Science Applications Subgroup discovery spatio-temporal analytics Bayesian non-parametric model Artificial intelligence business computer Information Systems |
| Zdroj: | Du, X, Pei, Y, Duivesteijn, W & Pechenizkiy, M 2020, ' Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling ', Data Mining and Knowledge Discovery, vol. 34, no. 5, pp. 1267-1290 . https://doi.org/10.1007/s10618-020-00674-z Data Mining and Knowledge Discovery, 34(5), 1267-1290. Springer Open |
| ISSN: | 1384-5810 |
| DOI: | 10.1007/s10618-020-00674-z |
| Popis: | Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an important task for applications like tourism recommendation, location prediction and urban planning. Properly accomplishing this task requires a model that allows for diverse behavioral patterns on each of the three aspects: spatial location, time, and text. In this paper, we address the following question: how to find representative subgroups of social posts, for which the spatio-temporal behavioral patterns are substantially different from the behavioral patterns in the whole dataset? Selection and evaluation are the two challenging problems for finding the exceptional subgroups. To address these problems, we propose BNPM: a Bayesian non-parametric model, to model spatio-temporal behavior and infer the exceptionality of social posts in subgroups. By training BNPM on a large amount of randomly sampled subgroups, we can get the global distribution of behavioral patterns. For each given subgroup of social posts, its posterior distribution can be inferred by BNPM. By comparing the posterior distribution with the global distribution, we can quantify the exceptionality of each given subgroup. The exceptionality scores are used to guide the search process within the exceptional model mining framework to automatically discover the exceptional subgroups. Various experiments are conducted to evaluate the effectiveness and efficiency of our method. On four real-world datasets our method discovers subgroups coinciding with events, subgroups distinguishing professionals from tourists, and subgroups whose consistent exceptionality can only be truly appreciated by combining exceptional spatio-temporal and exceptional textual behavior. |
| Databáze: | OpenAIRE |
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