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While traditional distance measures are often capable of properly describing similarity between objects, in some application areas there is still potential to fine-tune these measures with additional information provided in the data sets. In this work we combine such traditional distance measures for document analysis with link information between documents to improve clustering results. In particular, we test the effectiveness of geodesic distances as similarity measures under the spacedoi:10.1109/cidm.2011.5949449 dblp:conf/cidm/TekirMK11 fatcat:7ayn5gyfa5atpetzf4f6dxmy64