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Text clustering based on concept-relational decomposition

Antoon Bronselaer UGent, Saskia Debergh, Dirk Van Hyfte and Guy De Tré UGent (2010) ICL 2010 Proceedings. p.357-359
abstract
In this paper, a new approach on text clustering is proposed. Based on the concept-relational decomposition, a set of documents can be represented as a multirelation on a concept space C. An interesting property of this representation is that high cuts of the multirelation reveal highly relevant couples of concepts. It appears that each maximal component of a cut corresponds to a cluster on the condition that intra-component and inter-component dependencies are taken into account. The optimal cut is the cut for which the number of clusters best fits the estimated number of clusters. An experiment conducted on 550 Dutch documents shows that our method outperforms standard clustering algorithms that operate on a vector space.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
in
ICL 2010 Proceedings
pages
357 - 359
publisher
International Association on Online Engineering
place of publication
Vienna, Austria
conference name
Interactive Computer Aided Learning (ICL 2010)
conference location
Hasselt, Belgium
conference start
2010-09-15
conference end
2010-09-17
ISBN
9783899585414
language
English
UGent publication?
yes
classification
C1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1043293
handle
http://hdl.handle.net/1854/LU-1043293
date created
2010-09-17 12:08:22
date last changed
2017-01-02 09:52:18
@inproceedings{1043293,
  abstract     = {In this paper, a new approach on text clustering is proposed. Based on the concept-relational decomposition, a set of documents can be represented as a multirelation on a concept space C. An interesting property of this representation is that high cuts of the multirelation reveal highly relevant couples of concepts. It appears that each maximal component of a cut corresponds to a cluster on the condition that intra-component and inter-component dependencies are taken into account. The optimal cut is the cut for which the number of clusters best fits the estimated number of clusters. An experiment conducted on 550 Dutch documents shows that our method outperforms standard clustering algorithms that operate on a vector space.},
  author       = {Bronselaer, Antoon and Debergh, Saskia and Van Hyfte, Dirk and De Tr{\'e}, Guy},
  booktitle    = {ICL 2010 Proceedings},
  isbn         = {9783899585414},
  language     = {eng},
  location     = {Hasselt, Belgium},
  pages        = {357--359},
  publisher    = {International Association on Online Engineering},
  title        = {Text clustering based on concept-relational decomposition},
  year         = {2010},
}

Chicago
Bronselaer, Antoon, Saskia Debergh, Dirk Van Hyfte, and Guy De Tré. 2010. “Text Clustering Based on Concept-relational Decomposition.” In ICL 2010 Proceedings, 357–359. Vienna, Austria: International Association on Online Engineering.
APA
Bronselaer, A., Debergh, S., Van Hyfte, D., & De Tré, G. (2010). Text clustering based on concept-relational decomposition. ICL 2010 Proceedings (pp. 357–359). Presented at the Interactive Computer Aided Learning (ICL 2010), Vienna, Austria: International Association on Online Engineering.
Vancouver
1.
Bronselaer A, Debergh S, Van Hyfte D, De Tré G. Text clustering based on concept-relational decomposition. ICL 2010 Proceedings. Vienna, Austria: International Association on Online Engineering; 2010. p. 357–9.
MLA
Bronselaer, Antoon, Saskia Debergh, Dirk Van Hyfte, et al. “Text Clustering Based on Concept-relational Decomposition.” ICL 2010 Proceedings. Vienna, Austria: International Association on Online Engineering, 2010. 357–359. Print.