Clustering avatars behaviours from Virtual Worlds interactions
EntityUAM. Departamento de Ingeniería Informática
10.1145/2189736.2189743WI&C '12: Proceedings of the 4th International Workshop on Web Intelligence & Communities. ACM, 2012. Art. 4
Funded byThis work has been partly supported by: Spanish Ministry of Science and Education under the project TIN2010-19872
SubjectsGraph and overlapping clustering; Hierarchical clustering; Virtual Worlds; Informática
NoteThis 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/10.1145/2189736.2189743
Rights© 2012 ACM
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.
This item appears in the following Collection(s)
Showing items related by title, author, creator and subject.