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Reseach Article

Impact of Ontology based Approach on Document Clustering

by S.C. Punitha, K. Mugunthadevi, M. Punithavalli
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 22 - Number 2
Year of Publication: 2011
Authors: S.C. Punitha, K. Mugunthadevi, M. Punithavalli
10.5120/2556-3506

S.C. Punitha, K. Mugunthadevi, M. Punithavalli . Impact of Ontology based Approach on Document Clustering. International Journal of Computer Applications. 22, 2 ( May 2011), 22-26. DOI=10.5120/2556-3506

@article{ 10.5120/2556-3506,
author = { S.C. Punitha, K. Mugunthadevi, M. Punithavalli },
title = { Impact of Ontology based Approach on Document Clustering },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 22 },
number = { 2 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 22-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume22/number2/2556-3506/ },
doi = { 10.5120/2556-3506 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:22.234075+05:30
%A S.C. Punitha
%A K. Mugunthadevi
%A M. Punithavalli
%T Impact of Ontology based Approach on Document Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 22
%N 2
%P 22-26
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Document clustering is considered as an important tool in the fast developing information explosion era. It is the process of grouping text documents into category groups and has found applications in various domains like information retrieval, web or corporate information systems. Ontology-based computing is emerging as a natural evolution of existing technologies to cope with the information onslaught. This paper discusses the concepts behind ontology-based document clustering and compares the performance with existing traditional system. The results prove that introducing ontology concepts with document clustering is promising and improves clustering process.

References
  1. Cadez, I.V., Gaffney, S. and Smyth, P. (2000) A general probabilistic framework for clustering individuals and objects, Proc. 6th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Pp.140–149.
  2. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K. and Harshman, R. (1990) Indexing by Latent Semantic Analysis, Journal of the American Society of Information Science.
  3. Euzenat, J. and Shvaiko, P. (2007) Ontology Matching, Springer-Verlag. Berlin Heidelberg.
  4. Everitt, B.S., Landau, S. and Leese, M. (2001) Cluster Analysis, Oxford University Press, Fourth Edition.
  5. Goe, J., Tan P.N. and Cheng, H. (2006) Semi-supervised Clustering with Partial Background Information. In Proc. of SIAM International Conference on Data Mining, Bethesda, MD.
  6. Gruber, T.R. (1993) A translation approach to portable ontology specifications, Technical Report, KSL, Knowledge System Laboratory, Pp.92-71.
  7. Hotho A., Staab S. and Stumme G, (2003) WordNet improves text document clustering, Proc. of the SIGIR 2003 Semantic Web Workshop, Pp. 541-544.
  8. Karypis, G., Han, E.H. and Kumar, V. (1999) Chameleon: Hierarchical clustering using dynamic modeling. Computer, Vol. 32, No. 8, Pp. 68–75.
  9. Manning, C. and Schütze, H. (1999) Foundations of Statistical Natural Language Processing, MIT Press, Cambridge, MA.
  10. Sedding J. and Kazakov, D. (2004) WordNet-based text document clustering, Proc. of the 3rd Workshop on Robust Methods in Analysis of Natural Language Processing Data, Pp.104-113.
  11. Wu, Z. and Palmer, M. (1994) Verb Semantics and Lexical Selection, Proc. of the 32nd Annual Meeting of the Assoc. for Computational Linguistics, Pp. 133-138.
  12. Yang, X., Guo, D., Cao, X. and Zhou, J. (2008) Research on Ontology-Based Text Clustering, Proceedings of the 2008 Third International Workshop on Semantic Media Adaptation and Personalization, EEE Computer Society Washington, DC, USA.
  13. Yang, Y. and Pedersen, J.O. (1997) A Comparative Study on Feature Selection in Text Categorization, Proc. of the 14th International Conference on Machine Learning ICML.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering Document Clustering Ontology Similarity Measure Text Mining