Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5014
Title: Geodesic distances for web document clustering
Authors: Tekir, Selma
Mansmann, Florian
Keim, Daniel
Tekir, Selma
Izmir Institute of Technology. Computer Engineering
Keywords: Cluster analysis
Geodesic distances
Wikipedia
User interfaces
Web document clustering
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Tekir, S., Mansmann, F., and Keim, D. (2011, April 11-15). Geodesic distances for web document clustering. Paper presented at the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011. doi:10.1109/CIDM.2011.5949449
Abstract: 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 space assumption of spherical geometry in a 0-sphere. Our proposed distance measure is thus a combination of the cosine distance of the term-document 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 k-means algorithm on the English Wikipedia hyperlinked data set with both traditional cosine distance and our proposed geodesic distance. The effectiveness of our approach is measured by computing micro-precision values of the clusters based on the provided categorical information of each article. © 2011 IEEE.
Description: Symposium Series on Computational Intelligence, IEEE SSCI2011 - 2011 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011; Paris; France; 11 April 2011 through 15 April 2011
URI: http://doi.org/10.1109/CIDM.2011.5949449
http://hdl.handle.net/11147/5014
ISBN: 9781424499274
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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