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2010

Classification of Whereabouts Patterns From Large-scale Mobility Data

9 years 9 months ago
Classification of Whereabouts Patterns From Large-scale Mobility Data
Classification of users' whereabouts patterns is important for many emerging ubiquitous computing applications. Latent Dirichlet Allocation (LDA) is a powerful mechanism to extract recurrent behaviors and high-level patterns (called topics) from mobility data in an unsupervised manner. One drawback of LDA is that it is difficult to give meaningful and usable labels to the extracted topics. We present a methodology to automatically classify the topics with meaningful labels so as to support their use in applications. This mechanism is tested and evaluated using the Reality Mining dataset consisting of about 350000 hours of continuous data on human behavior.
Laura Ferrari, Marco Mamei
Added 15 Feb 2011
Updated 15 Feb 2011
Type Journal
Year 2010
Where WOA
Authors Laura Ferrari, Marco Mamei
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