Spectral partitioning with multiple eigenvectors
Discrete Applied Mathematics - Special volume on VLSI
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Spectral partitioning works: planar graphs and finite element meshes
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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With the rapid development of the Web2.0 communities, many researchers have been attracted by the concept of folksonomy from the field of data mining and information retrieval. Finding out semantic correlation of tags is avid requirement for web2.0 application. However, no proper algorithm can tackle this task very well. This paper proposes a core-tag oriented clustering method to handle the task. The main contributions include: (1) Proposing the concept of coretag oriented space; (2) Proposing a method called Core-Tag oriented Spectral Clustering (CTSC) to cluster tags in the new space; (3) Designing experiments to evaluate the algorithm, and the results show that CTSC algorithm performs well on clustering tags.