Open Access   Article Go Back

Deriving the Partial Order of Documents to Extend Clustering Applications

A. George Louis Raja1 , F. Sagayaraj Francis2 , P. Sugumar3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 424-430, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.424430

Online published on Jan 31, 2019

Copyright © A. George Louis Raja, F. Sagayaraj Francis, P. Sugumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: A. George Louis Raja, F. Sagayaraj Francis, P. Sugumar, “Deriving the Partial Order of Documents to Extend Clustering Applications,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.424-430, 2019.

MLA Style Citation: A. George Louis Raja, F. Sagayaraj Francis, P. Sugumar "Deriving the Partial Order of Documents to Extend Clustering Applications." International Journal of Computer Sciences and Engineering 7.1 (2019): 424-430.

APA Style Citation: A. George Louis Raja, F. Sagayaraj Francis, P. Sugumar, (2019). Deriving the Partial Order of Documents to Extend Clustering Applications. International Journal of Computer Sciences and Engineering, 7(1), 424-430.

BibTex Style Citation:
@article{Raja_2019,
author = {A. George Louis Raja, F. Sagayaraj Francis, P. Sugumar},
title = {Deriving the Partial Order of Documents to Extend Clustering Applications},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {424-430},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3522},
doi = {https://doi.org/10.26438/ijcse/v7i1.424430}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.424430}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3522
TI - Deriving the Partial Order of Documents to Extend Clustering Applications
T2 - International Journal of Computer Sciences and Engineering
AU - A. George Louis Raja, F. Sagayaraj Francis, P. Sugumar
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 424-430
IS - 1
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
327 187 downloads 122 downloads
  
  
           

Abstract

The exponential growth of text documents over the internet has paved the way for systematic document organization. It is widely accepted that the document clustering has augmented the information retrieval process to a greater extend. Basically all the text clustering algorithms tend to establish more appropriate clusters of text documents, and the accuracy of text clustering algorithms are measured based on cluster cohesion and separation. Keeping to the basic principle of clustering to minimize cohesion and maximize separation, all the algorithms deploy different strategies to generate better quality clusters. It is observed from the detailed literature survey that Classification, Categorization, Plagiarism Detection and Clustering are correlated. All these text mining tasks are performed based on indexing, searching or relating the key terms present in the documents. Moreover, all the text mining methods focuses on establishing the similarity or difference among the text documents, by which they perform their intended tasks. Hence, they tend to limit the application of clustering only to complement information retrieval task. This paper tries to present an algorithm to establish the partial order among the text documents and thus to extend the applications of clustering.

Key-Words / Index Term

clustering, partial ordering, classification, categorization, indexing

References

[1] 1.www.wikipdia.com/ Hierarchy
[2] 2.www.wikipedia.com/Poset- Wikipedia.html.
[3] 3.Michelangelo Ceci and Donato Malerba, “Classifying web documents in a hierarchyof categories: a comprehensive study”, Journal of Intelligent Information Systems, ISSN: 0925-9902, Volume 28, Issue 4, pp. 37-78, 2007.
[4] 4.W.T. Chuang, A. Tiyyagura, J. Yang and G. Giuffrida, “A fast algorithm for hierarchicaltext classification”,Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2000), pp. 409-418, New York , U.S.A, 2000.
[5] 5.S. D. Alessio, K. Murray, R. Schiaffino, and A. Kershenbau, “The effect of using hierarchical classifiers in text categorization”, Proceedings of the 6thInternationalConferenceonRecherchedInformationAssistdeparOrdinateur(RIAO2000), pp. 302-313, Paris, France,2000.
[6] 6.D. Koller and M. Sahami, “Hierarchically classifying documents using very few words”, Proceedings of the 14th International Conference onMachineLearning , pp. 170-178, California, U.S.A, 1997.
[7] 7.M.K. M. Rahman and Tony W. S. Chow, “Content based hierarchical document organization using multi layer hybrid network and tree structured features”, Expert Systems with Applications, ISSN: 2874-2881, Volume 37, 2010