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.
Classification of whereabouts patterns from large-scale mobility data / Ferrari, L.; Mamei, M.. - 621:(2010). (Intervento presentato al convegno 11th Workshop on Objects to Agents, WOA 2010 tenutosi a Rimini, ita nel 2010).
Classification of whereabouts patterns from large-scale mobility data
Mamei M.
2010
Abstract
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.
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