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Abstract:
This technical report 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 (e.g., k-means or
DBSCAN). These techniques result in higher cluster purity, better
overall accuracy, and make self-organization more robust. Our
comprehensive experiments on three different real-world corpora
demonstrate the benefits of our approach.
