Instanced-Based Mapping between Thesauri and Folksonomies
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Social Semantics and Its Evaluation by Means of Semantic Relatedness and Open Topic Models
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Adaptive learning resources search mechanism
Proceedings of the second ACM international workshop on Multimedia technologies for distance leaning
Clustering weblogs on the basis of a topic detection method
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Automatic tagging and geotagging in video collections and communities
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
An efficient algorithm for topic ranking and modeling topic evolution
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Near2me: an authentic and personalized social media-based recommender for travel destinations
WSM '11 Proceedings of the 3rd ACM SIGMM international workshop on Social media
Topic mining based on graph local clustering
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Tripartite community structure in social bookmarking data
The New Review of Hypermedia and Multimedia - Special issue on Social Linking and Hypermedia
Unsupervised topic detection model and its application in text categorization
Proceedings of the CUBE International Information Technology Conference
Data sets for offline evaluation of scholar's recommender system
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
A Graph Analytical Approach for Topic Detection
ACM Transactions on Internet Technology (TOIT)
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We consider topic detection without any prior knowledgeof category structure or possible categories. Keywords are extracted and clustered based on different similarity measures using the induced k-bisecting clustering algorithm. Evaluation on Wikipedia articles shows that clusters of keywords correlate strongly with the Wikipedia categories of the articles. In addition, we find that a distance measure based on the Jensen-Shannon divergence of probability distributions outperforms the cosine similarity. In particular, a newly proposed term distribution taking co-occurrence of terms into account gives best results.