Type of Publication:  Journal article 
Publication status:  Published 
Author:  Tekir, Selma; Mansmann, Florian; Keim, Daniel 
Year of publication:  2012 
Published in:  Journal of Artificial Intelligence and Soft Computing Research ; 2 (2012), 3.  pp. 247258.  eISSN 20832567 
Summary: 
The quality of a clustering not only depends on the chosen algorithm and its parameters, but also on the definition of the similarity of two respective objects in a dataset. Applications such as clustering of web documents is traditionally built either on textual similarity measures or on link information. Due to the incompatibility of these two information spaces, combining these two information sources in one distance measure is a challenging issue. In this paper, we thus propose a geodesic distance function that combines traditional similarity measures with link information. In particular, we test the effectiveness of geodesic distances as similarity measures under the space assumption of spherical geometry in a 0sphere. Our proposed distance measure is thus a combination of the cosine distance of the termdocument matrix and some curvature values in the geodesic distance formula. To estimate these curvature values, we calculate clustering coefficient values for every document from the link graph of the data set and increase their distinctiveness by means of a heuristic as these clustering coefficient values are rough estimates of the curvatures. To evaluate our work, we perform clustering tests with the kmeans algorithm on a subset of the EnglishWikipedia hyperlinked data set with both traditional cosine distance and our proposed geodesic distance. Additionally, taking inspiration from the unified view of the performance functions of kmeans and kharmonic means, min and harmonic average of the cosine and geodesic distances are taken in order to construct alternate distance forms. The effectiveness of our approach is measured by computing microprecision values of the clusters based on the provided categorical information of each article.

Subject (DDC):  004 Computer Science 
Bibliography of Konstanz:  Yes 
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TEKIR, Selma, Florian MANSMANN, Daniel KEIM, 2012. Geodesic distances for clustering linked text data. In: Journal of Artificial Intelligence and Soft Computing Research. 2(3), pp. 247258. eISSN 20832567
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