Document Clustering with K-tree

Authors:
Christopher M. Vries;Shlomo Geva
Affiliations:
Faculty of Science and Technology, Queensland University of Technology, Brisbane, Australia;Faculty of Science and Technology, Queensland University of Technology, Brisbane, Australia
Venue:
Advances in Focused Retrieval
Year:
2009

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Abstract

This paper describes the approach taken to the XML Mining track at INEX 2008 by a group at the Queensland University of Technology. We introduce the K-tree clustering algorithm in an Information Retrieval context by adapting it for document clustering. Many large scale problems exist in document clustering. K-tree scales well with large inputs due to its low complexity. It offers promising results both in terms of efficiency and quality. Document classification was completed using Support Vector Machines.