Provides a Hybrid Method of Analyzing News Databases Using RapidMine Case Study: Persian News Texts

Document Type : Original Article

Authors

1 fava/sadr

2 -

Abstract

One of the most widely used approaches in the management of Web-based systems, use the online agency and Data related to them . Online web agency, like the heart of a community and is one of the most critical information resources in a society. The importance of this topic in the news military organizations will be doubled. So having a right way and a unique conceptual approach to analyze and categorize the resources, Can provide numerous benefits and assistance to decision-makers within the organization, especially military organizations in carrying out decisions. In this study, firstly we contribute to the study and understanding of the concepts of data mining methods. Secondly, analysis of different methods of text processing must be done from different perspectives So as to identify the positive strategies and strengths of a variety of methods and The Finally, the analysis of news database, so as to uncover information hidden in the data and how to use it. Keywords: Data mining, text mining, News Database, Mixed-Method.

Keywords


[1] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI magazine, vol. 17, p. 37, 1996.##
[2] J. Han and M. Kamber, “Data Mining,” Southeast Asia Edition: Concepts and Techniques: Morgan kaufmann, 2006.##
[3] M. J. Berry and G. S. Linoff, “Data mining,” techniques: for marketing, sales, and customer relationship management: John Wiley & Sons, 2004.##
[4] H. C. Koh and C. K. Low, “Going concern prediction using data mining techniques,” Managerial Auditing Journal, vol. 19, pp. 462-476, 2004.##
[5] N. Aggarwal, A. Kumar, H. Khatter, and V. Aggarwal, “Analysis the effect of data mining techniques on database,” Advances in Engineering Software, vol. 47, pp. 164-169, 2012.##
[6] D. T. Larose, “k‐Nearest Neighbor Algorithm,” Discovering Knowledge in Data: An Introduction to Data Mining, pp. 90-106, 2005.##
[7] M. S. Deshpande and D. V. Thakare, “Data mining system and applications: A review,” International Journal of Distributed and Parallel systems (IJDPS), vol. 1, pp. 32-44, 2010.##
[8] Y. M. Chae, S. H. Ho, K. W. Cho, D. H. Lee, and S. H. Ji, “Data mining approach to policy analysis in a health insurance domain,” International journal of medical informatics, vol. 62, pp. 103-111, 2001.##
[9] R. Alguliev and R. Aliguliyev, “Experimental investigating the F-Measure as similarity measure for automatic text summarization,” Applied and Computational Mathematics, vol. 6, no. 2, pp. 278-287, 2007.##
[10] M. E. Califf and R. J. Mooney, “Bottom-up relational learning of pattern matching rules for information extraction,” Journal of Machine Learning Research, vol. 4, pp. 177–210, 2003.##
[11] M. A. Hearst, “Untangling text data mining,” In Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, Association for Computational Linguistics, pp. 3-10, June 1999.##
[12] U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “Knowledge discovery and data mining: Towards a unifying framework,” In Knowledge Discovery and Data Mining, pp. 82–88, 1996.##
[13] V. Kumar and M. Joshi, What is datamining?, 2003. http://wwwusers.cs.umn.edu /~mjoshi/hpdmtut/sld004.htm##
[14] R. Feldman and I. Dagan, “Kdt – knowledge discovery in texts,” In Proc. Of the First Int. Conf. on Knowledge Discovery (KDD), pp. 112–117, 1995.##
[15] G. Salton, A. Wong, and C. S. Yang, “A vector space model for automatic indexing,” Communications of the ACM, vol. 18(11), pp. 613–620, 1975. (see also TR74-218, Cornell University, NY, USA).##
[16] S. E. Robertson, “The probability ranking principle,” Journal of Documentation, vol. 33, pp. 294–304, 1977.##
[17] C. J. Van Rijsbergen, “A non-classical logic for information retrieval,” The Computer Journal, vol. 29(6), pp. 481–485, 1986.##
[18] H. Zhuge et al., “An Automatic Semantic Relationships Discovery Approach,” The 13th International World Wide Web Conference (WWW2004), New York, USA, May 2004.##
[19] T. Joachims, “Text categorization with support vector machines: Learning with many relevant features,” In C. Nedellec and C. Rouveirol, editors, European Conf. on Machine Learning (ECML), 1998.##
[20] S. Dumais, J. Platt, D. Heckerman, and M. Sahami, “Inductive learning algorithms and representations for text categorization,” In 7th Int. Conf. on Information and Knowledge I, 1998.##
[21] K. Nigam, A. McCallum, S. Thrun, and T. Mitchell, “Text classification from labeled and unlabeled documents using em,” Machine Learning, vol. 39, pp. 103–134, 2000.##
[22] F. Sebastiani, “Machine learning in automated text categorization,” ACM Computing Surveys, vol. 34, pp. 1–47, 2002.##
[23] N. Kanya and S. Geetha, “Information Extraction,” A Text Miningap Proach,” produced IEEE, 2007.##
[24] H. Karanikas, C. Tjortjis, and B. theodoulidis, “An approach to text mining information exteraction,” 2001.##
[25] M. Rajman, “Text Mining, Knowledge extraction from unstructured texual data,” Proc. of Eurostat Conference, Francfort (Deutchland), May 1997.##