Markov random field modeling in image analysis
Markov random field modeling in image analysis
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
A comparative evaluation of different link types on enhancing document clustering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
Exploit the tripartite network of social tagging for web clustering
Proceedings of the 18th ACM conference on Information and knowledge management
Document clustering of scientific texts using citation contexts
Information Retrieval
Costco: robust content and structure constrained clustering of networked documents
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Leveraging network structure for incremental document clustering
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Proceedings of the Third Symposium on Information and Communication Technology
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This paper addresses the problem of automatically structuring linked document collections by using clustering. In contrast to traditional clustering, we study the clustering problem in the light of available link structure information for the data set (e.g., hyperlinks among web documents or co-authorship among bibliographic data entries). Our approach is based on iterative relaxation of cluster assignments, and can be built on top of any clustering algorithm. This technique results in higher cluster purity, better overall accuracy, and make self-organization more robust.