Clustering avatars behaviours from Virtual Worlds interactions
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
ACMFecha de edición
2012Cita
10.1145/2189736.2189743
WI&C '12: Proceedings of the 4th International Workshop on Web Intelligence & Communities. ACM, 2012. Art. 4
ISBN
978-1-4503-1189-2DOI
10.1145/2189736.2189743Financiado por
This work has been partly supported by: Spanish Ministry of Science and Education under the project TIN2010-19872Versión del editor
http://doi.acm.org/10.1145/2189736.2189743Materias
Graph and overlapping clustering; Hierarchical clustering; Virtual Worlds; InformáticaNota
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 4th International Workshop on Web Intelligence & Communities, http://dx.doi.org/10.1145/2189736.2189743Derechos
© 2012 ACMResumen
Virtual Worlds (VWs) platforms and applications provide a practical implementation of the Metaverse concept. These applications, as highly inmersive and interactive 3D environments, have become very popular in social networks and games domains. The existence of a set of open platforms like OpenSim or OpenCobalt have played a major role in the popularization of this technology and they open new exciting research areas. One of these areas is behaviour analysis. In virtual world, the user (or avatar) can move and interact within an artificial world with a high degree of freedom. The movements and iterations of the avatar can be monitorized, and hence this information can be analysed to obtain interesting behavioural patterns. Usually, only the information related to the avatars conversations (textual chat logs) are directly available for processing. However, these open platforms allow to capture other kind of information like the exact position of an avatar in the VW, what they are looking at (eye-gazing) or which actions they perform inside these worlds. This paper studies how this information, can be extracted, processed and later used by clustering methods to detect behaviour or group formations in the world. To detect the behavioural patterns of the avatars considered, clustering techniques have been used. These techniques, using the correct data preprocessing and modelling, can be used to automatically detect hidden patterns from data.
Lista de ficheros
Google Scholar:Bello Orgaz, Gema
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R-Moreno, María Dolores
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Camacho, David
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Barrero, David F.
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