Document Clustering with K-tree

TL;DR

This paper introduces the K-tree clustering algorithm adapted for document clustering in large-scale information retrieval, demonstrating its efficiency and quality improvements over existing methods.

Contribution

The paper presents a novel adaptation of the K-tree algorithm for document clustering, emphasizing its scalability and effectiveness in large datasets.

Findings

K-tree scales efficiently with large datasets

K-tree provides promising clustering quality

Support Vector Machines used for document classification

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.