RATC: A Robust Automated Tag Clustering Technique
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Spectral Clustering in Social-Tagging Systems
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Tag disambiguation through Flickr and Wikipedia
DASFAA'10 Proceedings of the 15th international conference on Database systems for advanced applications
A graph-based clustering scheme for identifying related tags in folksonomies
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Co-clustering analysis of weblogs using bipartite spectral projection approach
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
A local information passing clustering algorithm for tagging systems
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
APPECT: an approximate backbone-based clustering algorithm for tags
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Bi-clustering gene expression data using co-similarity
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
In & out zooming on time-aware user/tag clusters
Journal of Intelligent Information Systems
A sparsity-inducing formulation for evolutionary co-clustering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
The Dynamics of Content Popularity in Social Media
International Journal of Data Warehousing and Mining
Contextual object category recognition for RGB-D scene labeling
Robotics and Autonomous Systems
| Hi-index | 0.00 |
Under social tagging systems, a typical Web 2.0 application, users label digital data sources by using freely chosen textual descriptions (tags). Poor retrieval in the aforementioned systems remains a major problem mostly due to questionable tag validity and tag ambiguity. Earlier clustering techniques have shown limited improvements, since they were based mostly on tag co-occurrences. In this paper, a co-clustering approach is employed, that exploits joint groups of related tags and social data sources, in which both social and semantic aspects of tags are considered simultaneously. Experimental results demonstrate the efficiency and the beneficial outcome of the proposed approach in correlating relevant tags and resources.