Genetic Mining: Using Genetic Algorithm for Topic based on Concept Distribution
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33092
Genetic Mining: Using Genetic Algorithm for Topic based on Concept Distribution

Authors: S. M. Khalessizadeh, R. Zaefarian, S.H. Nasseri, E. Ardil

Abstract:

Today, Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to text classification, summarization and information retrieval system in text mining process. This researches show a better performance due to the nature of Genetic Algorithm. In this paper a new algorithm for using Genetic Algorithm in concept weighting and topic identification, based on concept standard deviation will be explored.

Keywords: Genetic Algorithm, Text Mining, Term Weighting, Concept Extraction, Concept Distribution.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074845

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3710

References:


[1] T. Anand and G. Kahn, "Opportunity explorer: Navigating large databases using knowledge discovery templates", In Proceedings of the 1993 workshop on Knowledge Discovery in Databases.
[2] C. Apte, F. Damerau and S.M. Weiss, "Automated learning of decision rules for text categorization", ACM Transactions on Information Systems, 12 (1994) 233-251.
[3] C. Blake, W. Pratt, B. Rules and F. Features, "A Semantic Approach to Selecting Features from Text--, ICDM, (2001) 59-66.
[4] G. Brown and G. Yule, Discourse Analysis. Cambridge University Press, 1983.
[5] S. Chakrabarti, ÔÇÿÔÇÿData mining for hypertext: a tutorial survey", ACM SIGKDD explorations, 1 (2000) 1-11.
[6] C. Clifton, R. Cooley and J. Rennie, T. Cat, ÔÇÿÔÇÿData mining for topic identi_cation in a text corpus--, 3rd European Conference of Practice of Knowledge Discovery in Databases, Prague, Czech Republic, 1999.
[7] K. Ezawa and S. Norton, "Knowledge discovery in telecommunication services data using Bayesian Models", In Proceedings of the First International Conference on Knowledge Discovery (KDD-95), 1993.
[8] W. Fan, M.D. Gordon and P. Pathak, "A generic ranking function discovery framework by genetic programming for information retrieval", Information Processing and Management 40 (2004) 587-602.
[9] H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining, Kluwer Academic Publishers, 1998.
[10] I. Mani and M.T. Maybury, Advances in Automatic Text Summarization, MIT Press, 1999.
[11] T. W. Manikas and M.H. Mickle, ÔÇÿÔÇÿA genetic algorithm for mixed macro and standard cell placement", 27th ACM IEEE Design Automation Conference.
[12] T. Nasukawa and T. Nagano, ÔÇÿÔÇÿText analysis and knowledge mining system", IBM SYSTEMS JOURNAL, VOL 40, NO 4, 2001.
[13] S.N. Sancheza, E. Triantaphylloua, J. Chenb and T. W. Liaoa, "An incremental learning algorithm for constructing Boolean functions from positive and negative examples", Computers & Operations Research 29 (2002) 1677-1700.
[14] C.N. Silla, G.L. Pappa, A. Freitas and C.A. Kaestner, "Automatic text summarization with genetic algorithm-based attribute selection", 9th Ibero-American Conference on AL, Lecture Notes in Computer Science, 3315 (2004) 305-314.
[15] S.S. Weng, Y.J. Lin and F. Jen, ÔÇÿÔÇÿA study on searching for similar documents based on multiple concepts and distribution of concepts", Expert Systems with Applications 25 (2003) 355-368.
[16] M. Mitchell, An Introduction to Genetic Algorithm, MIT Press, 1996.
[17] G.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, New York, 1989.