NDLI: TOPTRAC: Topical Trajectory Pattern Mining

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TOPTRAC: Topical Trajectory Pattern Mining

Content Provider	ACM Digital Library
Author	Kim, Younghoon Yuan, Cangzhou Han, Jiawei
Abstract	With the increasing use of GPS-enabled mobile phones, geo-tagging, which refers to adding GPS information to media such as micro-blogging messages or photos, has seen a surge in popularity recently. This enables us to not only browse information based on locations, but also discover patterns in the location-based behaviors of users. Many techniques have been developed to find the patterns of people's movements using GPS data, but latent topics in text messages posted with local contexts have not been utilized effectively. In this paper, we present a latent topic-based clustering algorithm to discover patterns in the trajectories of geo-tagged text messages. We propose a novel probabilistic model to capture the semantic regions where people post messages with a coherent topic as well as the patterns of movement between the semantic regions. Based on the model, we develop an efficient inference algorithm to calculate model parameters. By exploiting the estimated model, we next devise a clustering algorithm to find the significant movement patterns that appear frequently in data. Our experiments on real-life data sets show that the proposed algorithm finds diverse and interesting trajectory patterns and identifies the semantic regions in a finer granularity than the traditional geographical clustering methods.
Starting Page	587
Ending Page	596
Page Count	10
File Format	PDF
ISBN	9781450336642
DOI	10.1145/2783258.2783342
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Modeling geo-tagged messages Topical trajectory pattern
Content Type	Text
Resource Type	Article

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