Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
Graph-based text classification: learn from your neighbors
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Knowing a web page by the company it keeps
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A neighborhood-based approach for clustering of linked document collections
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Efficient top-k querying over social-tagging networks
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
The topic-perspective model for social tagging systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting tag and word correlations for improved webpage clustering
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Demand-driven tag recommendation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Extracting the mesoscopic structure from heterogeneous systems
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
User-related tag expansion for web document clustering
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Latent subject-centered modeling of collaborative tagging: An application in social search
ACM Transactions on Management Information Systems (TMIS)
Detecting communities in K-partite K-uniform (hyper)networks
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Tripartite community structure in social bookmarking data
The New Review of Hypermedia and Multimedia - Special issue on Social Linking and Hypermedia
Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering
ACM Transactions on Intelligent Systems and Technology (TIST)
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In this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method, called "Tripartite Clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. The proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. We also compare the proposed clustering approach with K-means. All the clustering results are evaluated against a human-maintained web directory. The experimental results show that Tripartite Clustering significantly outperforms the content-based K-means approach and achieves performance close to that of social annotation-based K-means whereas generating much more useful information.