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Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include \u201cgoing to work late\u201d, \u201cgoing home early\u201d, \u201cworking nonstop\u201d and \u201chaving no reception (phone off)\u201d at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as \u201cgoing to work early\u201d or \u201cturning off the mobile (or having no reception) in the evenings\u201d. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events.<\/jats:p>","DOI":"10.1145\/1889681.1889684","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T20:56:02Z","timestamp":1350075362000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":164,"title":["Discovering routines from large-scale human locations using probabilistic topic models"],"prefix":"10.1145","volume":"2","author":[{"given":"Katayoun","family":"Farrahi","sequence":"first","affiliation":[{"name":"IDIAP Research Institute, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Gatica-Perez","sequence":"additional","affiliation":[{"name":"IDIAP Research Institute, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2011,1,24]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1280940.1280982"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS).","author":"Blei D. 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