A Neighborhood-Based Approach for Clustering of Linked Document Collections

Angelova, Ralitsa; Siersdorfer, Stefan; Yu, Philip S.; Tsotras, Vassilis J.; Fox, Edward A.; Liu, Bing

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A Neighborhood-Based Approach for Clustering of Linked Document Collections

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Angelova, Ralitsa
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Siersdorfer, Stefan
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Angelova, R., & Siersdorfer, S. (2006). A Neighborhood-Based Approach for Clustering of Linked Document Collections. In Proceedings of the Conference on Information and Knowledge Management, CIKM 2006 (pp. 778-779). New York, USA: ACM.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-21FE-0

Abstract

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.