CFP last date
22 April 2024
Reseach Article

A Survey Paper on Concept Mining in Text Documents

by K. N. S. S. V. Prasad, S. K. Saritha, Dixa Saxena
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 166 - Number 11
Year of Publication: 2017
Authors: K. N. S. S. V. Prasad, S. K. Saritha, Dixa Saxena
10.5120/ijca2017914143

K. N. S. S. V. Prasad, S. K. Saritha, Dixa Saxena . A Survey Paper on Concept Mining in Text Documents. International Journal of Computer Applications. 166, 11 ( May 2017), 7-10. DOI=10.5120/ijca2017914143

@article{ 10.5120/ijca2017914143,
author = { K. N. S. S. V. Prasad, S. K. Saritha, Dixa Saxena },
title = { A Survey Paper on Concept Mining in Text Documents },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 166 },
number = { 11 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume166/number11/27711-2017914143/ },
doi = { 10.5120/ijca2017914143 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:13:24.240225+05:30
%A K. N. S. S. V. Prasad
%A S. K. Saritha
%A Dixa Saxena
%T A Survey Paper on Concept Mining in Text Documents
%J International Journal of Computer Applications
%@ 0975-8887
%V 166
%N 11
%P 7-10
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Concept Mining has become an important research area. Concept Mining is used to search or extract the concepts embedded in the text document. Concept based approach search for the informative terms based on their meaning rather than on the presence of the keyword in the text.

References
  1. Berry Michael W., (2004), “Automatic Discovery of Similar Words”, in “Survey of Text Mining: Clustering, Classification and Retrieval”, Springer Verlag, New York, LLC, 24-43
  2. Navathe, Shamkant B., and Elmasri Ramez, (2000), “Data Warehousing and Data Mining”, in “Fundamentals of Database Systems”, Pearson Education pvtInc, singapore, 841-872.
  3. HaralamposKaranikas and BabisTheodoulidis Manchester, (2001), “Knowledge Discovery in Text and Text Mining Software”, Centre for Research in Information Management, UK
  4. https://en.wikipedia.org/wiki/Concept_mining
  5. P. Kingsbury and M. Palmer, “Propbank: The Next Level of Treebank,” Proc. Workshop Treebanks and Lexical Theories, 2003.
  6. G. Salton and C. Buckley. Term Weighting Approaches in AutomaticText Retrieval, 1960, Information Processing and Management, 24, Vol5, 513-52
  7. G. Salton and C. Buckley. Term Weighting Approaches in AutomaticText Retrieval, 1960, Information Processing and Management, 24, Vol 5, 513-523
  8. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, 1994
  9. Agrawal R, Imielinski T, Swami A, “Mining association rules between sets of items in large databases”. Proc of the 1993ACM SIGMODInternational Conference on Management of data
  10. Bing Liu, Yiming Ma, “Discovering unexpected information from your competitors ‘Web Sites in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 26-29, 2001, San Francisco, USA.
  11. An Efficient Concept-Based Mining Model for Enhancing Text Clustering, Shady Shehata, Member, IEEE, FakhriKarray, Senior Member, IEEE, and Mohamed S. Kamel, Fellow, IEEE 2010
  12. Concept mining from natural language texts, Rockai V.  Dept. of Cyber. & Artificial Intelligent, Tech. Univ. of Kosice, Kosice, Slovakia Mach. M IEEE 2012
  13. Concept Mining using Association Rules and Combinatorial Topology Sutojo, A, San Jose State University, San Jose IEEE 2007
  14. Webpage Clustering and Concept Mining, an Approach to Intelligent Information Retrieval. Fang Li, Martin Mehlitz, Li Feng, Huanye Sheng, DEPT of CSE, Shanghai Jiaotong University, Shanghai ,China IEEE 2006
Index Terms

Computer Science
Information Sciences

Keywords

Concept mining Term Frequency Inverse Document Frequency Conceptual Term Frequency