Volume 11 : Number 2 : Paper 7

December 2008 Special Issue of Best Papers presented at CLEI 2007, San Jose, Costa Rica
Measuring Contribution of HTML Features in Web Document Clustering

Authors and Affiliations:
Esteban Meneses, Centro de Investigaciones en Computaci
Oldemar Rodriguez-Rojas, Universidad de Costa Rica, Escuela de Matematica, Costa Rica

Documents in HTML format have many features to analyze, from the terms in special sections to the phrases that appear in the whole document. However, it is important to decide which feature contributes the most to separate documents according to classes. Given this information, it is possible not to include certain feature in the representation for the document, given that it is expensive to compute and does not contribute enough
in the clustering process. By using a novel representation model and the standard k-means algorithm, we discovered that terms in the body of document contributes the most, followed by terms in other sections. Suffix tree provides poor contribution in that scenario, while term order graphs influence a little the partition. We used 4 known datasets to support the conclusions.

Received April, 2007, Revised Dec, 2008 , Editor: Manuel Bermudez, Marcelo Jenkins
Full paper, 12 pages [ PDF, 425 Kb ]