Clustering with random indexing K-tree and XML structure

De Vries, Christopher, Geva, Shlomo, & De Vine, Lance (2010) Clustering with random indexing K-tree and XML structure. In Geva, S, Kamps, J, & Trotman, A (Eds.) Focused Retrieval and Evaluation: 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2009, Revised and Selected Papers [Lecture Notes in Computer Science, Volume 6203]. Springer, Germany, pp. 407-415.

This is the latest version of this eprint.

Preview

Accepted Version (PDF 250kB)
c33656.pdf.

View at publisher

Description

This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.

Impact and interest:

3 citations in Scopus

2 citations in Web of Science®

Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

333 since deposited on 01 Aug 2010

19 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

More statistics...

ID Code:

33656

Item Type:

Chapter in Book, Report or Conference volume (Conference contribution)

ORCID iD:

Geva, Shlomo	orcid.org/0000-0003-1340-2802

Measurements or Duration:

9 pages

Keywords:

Clustering, Documents, INEX, K-tree, Mining, Random Indexing, Random Projection, Structure, XML

DOI:

10.1007/978-3-642-14556-8_40

ISBN:

978-3-642-14555-1

Pure ID:

32143560

Divisions:

Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > QUT Faculties & Divisions > Science & Engineering Faculty
Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > Research Centres > High Performance Computing and Research Support

Consult author(s) regarding copyright matters

This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au

Deposited On:

01 Aug 2010 23:57

Last Modified:

02 Mar 2024 00:09

Available Versions of this Item

Clustering with random indexing K-tree and XML structure. (deposited 02 Aug 2010 01:08)
- Clustering with random indexing K-tree and XML structure. (deposited 01 Aug 2010 23:57) [Currently Displayed]

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page

CORE (COnnecting REpositories)