Ranking based clustering for social event detection

Sutanto, Taufik & Nayak, Richi (2014) Ranking based clustering for social event detection. In Larson, Martha, Ionescu, Bogdan, Anguera, Xavier, Eskevich, Maria, Korshunov, Pavel, Schedl, Markus, et al. (Eds.) Working Notes Proceedings of the MediaEval 2014 Workshop, CEUR Workshop Proceedings, Barcelona, Spain, pp. 1-2.

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The problem of clustering a large document collection is not only challenged by the number of documents and the number of dimensions, but it is also affected by the number and sizes of the clusters. Traditional clustering methods fail to scale when they need to generate a large number of clusters. Furthermore, when the clusters size in the solution is heterogeneous, i.e. some of the clusters are large in size, the similarity measures tend to degrade. A ranking based clustering method is proposed to deal with these issues in the context of the Social Event Detection task. Ranking scores are used to select a small number of most relevant clusters in order to compare and place a document. Additionally,instead of conventional cluster centroids, cluster patches are proposed to represent clusters, that are hubs-like set of documents. Text, temporal, spatial and visual content information collected from the social event images is utilized in calculating similarity. Results show that these strategies allow us to have a balance between performance and accuracy of the clustering solution gained by the clustering method.

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ID Code: 78768
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Clustering a large document collection, Social Event Detection, Ranking scores
ISSN: 1613-0073
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 18 Nov 2014 23:03
Last Modified: 20 Nov 2014 04:52

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