TY  - CONF
AU  - Lu, Caimei
AU  - Chen, Xin
AU  - Park, E. K.
A2  - 
T1  - Exploit the tripartite network of social tagging for web clustering
T2  - Proceeding of the 18th ACM conference on Information and knowledge management
PB  - ACM
C1  - New York, NY, USA
PY  - 2009/
CY  -  
VL  - 
IS  - 
SP  - 1545
EP  - 1548
UR  - http://doi.acm.org/10.1145/1645953.1646167
DO  - 10.1145/1645953.1646167
KW  - clustering
KW  - bachelor:2011:bachmann
KW  - web
L1  - 
SN  - 978-1-60558-512-3
N1  - 
N1  - 
AB  - 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.
ER  -

TY  - CONF
AU  - Lu, Caimei
AU  - Chen, Xin
AU  - Park, E. K.
A2  - 
T1  - Exploit the tripartite network of social tagging for web clustering
T2  - CIKM '09: Proceeding of the 18th ACM conference on Information and knowledge management
PB  - ACM
C1  - New York, NY, USA
PY  - 2009/
CY  -  
VL  - 
IS  - 
SP  - 1545
EP  - 1548
UR  - http://portal.acm.org/citation.cfm?id=1646167&dl=GUIDE&coll=GUIDE&CFID=93888742&CFTOKEN=72927742
DO  - 10.1145/1645953.1646167
KW  - tagging
KW  - clustering
KW  - mode
KW  - three
KW  - network
KW  - triadic
L1  - 
SN  - 978-1-60558-512-3
N1  - 
N1  - 
AB  - 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.
ER  -