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A Framework for Knowledge Discovery in the News Media Using Text Mining Technique

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Abstract (2. Language): 
The increase in the media information available to the news audience is growing at an alarming rate and this calls for an efficient means of analyzing and mining information that can be used in making vital decisions. This is most especially true in Nigeria where there is an alarming increase in the crime rate that needs to be analyzed. The work aimed at the use of text mining techniques for deducing the rate of occurrence of crime in Nigeria cities. A review of various text mining techniques with their comparative advantages is presented. The work modeled a text mining framework for the analysis, extraction and organization of news media objects. Mathematical and algorithmic text mining techniques were applied in the development of the crime analysis system. Information was captured from online news material and on ontology was built for representation of specific domain of crime using frames knowledge representation language and Protégé ontology builder. The k-means algorithm is used for knowledge discovery to extract relevant information from the news corpus. The applied clustering technique showcased resulted patterns with graphical representation that aids deductions of the rate of occurrence of crimes. In conclusion, the approach is found useful for sociological analysis of occurrence of crime which invariably aids decision makers on national security.



[1] I.T. Fatudimu, A.G. Musa, C.K. Ayo, A.B. Sofoluwe, “Knowledge
Discovery in Online Repositories: A Text Mining Approach”,
European Journal of Scientific Research 2008
[2] R. Feldman and J. Sanger, “The Text Mining Handbook”, Cambridge
University Press, 2007.
[3] R. Feldman, I. Dagan, “Knowledge Discovery in Textual Databases
(KDT), Math and Computer Sceince Dept. Bar-Ilan University
Ramat-Gan, ISRAEL 52900, 1997
[4] S. Bloehdorn, P.Cimiano, A. Htho, S.Staab, Institure AIFB
University of Kalruhe, KDE Group University of Kassel, 2004.
[5] R.M. Patton, C.C. Rojas, B.G. Beckerman, T.E. Potock, “A
Computational Framework for Search, Discovery and Trending of
Patient Health in Radiology Reports”, Computational Sciences and
Engineering Division Oak Ridge National Laboratory Oak Ridge,
TN, USA, 2011
[6] S. Sudharhar, R. Fanzosi, N. Cristianini, “Automating Quantitative
Narrative Analysis of News Data”, JMLR: Workshop and Conference
proceedings 17(2011) 63 2nd Workshop on Applications of Pattern
Analysis, 2006
[7] D.S. Rajpu, R.S. Thakur, G.S. Thakur, “Rule Generation from
Textual Data by using Graph Based Apporach”, International Journal
of Computer Applications, 2011
[8] K. Norvag, R. Oyri, “News Item Extraction for Text Mining in Web
Newspapers”, Department of Computer and Information Science
Nowergian University of Science and Technology, 2004
[9] R. B. Allen, “Improving Access to Digitized Historical Newspapers
with Text Mining, Coordinated Models, and Formative User Interface
Design”, College of Information Science and Technology Drexel
University, Philadelphia, 2010
[10] S. Lee, J. Song, Y. Kim, “An Empirical Comparison of Four Text
Mining Methods”, Journal of Computer Information Systems, 2010.
[11] A.K. Ojo, A.B. Adeyemo, Framework for Knowledge Discovery
from Journal Articles Using Test Mining techniques, African Journal
of Computing & ICT,vol. 5, pp.33-42, 2012.
[12] M. Aiello and A.Pegoretti, “Textual Article Clustering in Newspaper
Pages”, Dept. of Information and Communication Technologies,
Universita di Trento, 2006.
[13] M. Steinbach, G. Karypis and V. Kumar, “A Comparison of
Document Clustering Techniques”, Department of Computer Science
and Engineering, University of Minnesota, 2000.
[14] N. Sumathi and V. Chittu, “A Modified Genetic Algorithm
Initializing K-Means Clustering”, Global Journal of Computer
Science and Technology, 2011.
[15] C. Luo, Y. Li, and M. Chung, “Text document clustering based on
neighbors”. Data & Knowledge Engineering, vol. 68, 2009.
[16] T. K. Landauer, P. W. Foltz and D. Laham, “an Introduction to Latent
Semantic Analysis”, Department of Psychology, Campus Box 345,
University of Colorado, 1998.
[17] S.E. Shaeffer, “Graph Clustering”, Laboratory for Theoretical
Computer Science, Hesinki University of technology, Finland, 2007.

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