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.

@inproceedings{Lu:2009:ETN:1645953.1646167, author = {Lu, Caimei and Chen, Xin and Park, E. K.}, title = {Exploit the tripartite network of social tagging for web clustering}, booktitle = {Proceeding of the 18th ACM conference on Information and knowledge management}, series = {CIKM '09}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, pages = {1545--1548}, url = {http://doi.acm.org/10.1145/1645953.1646167}, doi = {10.1145/1645953.1646167}, isbn = {978-1-60558-512-3}, keywords = {clustering, bachelor:2011:bachmann, web}, abstract = {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.} }

%0 = inproceedings %A = Lu, Caimei and Chen, Xin and Park, E. K. %B = Proceeding of the 18th ACM conference on Information and knowledge management %C = New York, NY, USA %D = 2009 %I = ACM %T = Exploit the tripartite network of social tagging for web clustering %U = http://doi.acm.org/10.1145/1645953.1646167

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.

@inproceedings{lu2009exploit, author = {Lu, Caimei and Chen, Xin and Park, E. K.}, title = {Exploit the tripartite network of social tagging for web clustering}, booktitle = {CIKM '09: Proceeding of the 18th ACM conference on Information and knowledge management}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, pages = {1545--1548}, url = {http://portal.acm.org/citation.cfm?id=1646167&dl=GUIDE&coll=GUIDE&CFID=93888742&CFTOKEN=72927742}, doi = {10.1145/1645953.1646167}, isbn = {978-1-60558-512-3}, keywords = {tagging, clustering, mode, three, network, triadic}, abstract = {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.} }

%0 = inproceedings %A = Lu, Caimei and Chen, Xin and Park, E. K. %B = CIKM '09: Proceeding of the 18th ACM conference on Information and knowledge management %C = New York, NY, USA %D = 2009 %I = ACM %T = Exploit the tripartite network of social tagging for web clustering %U = http://portal.acm.org/citation.cfm?id=1646167&dl=GUIDE&coll=GUIDE&CFID=93888742&CFTOKEN=72927742