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Bio-Inspired Techniques in the Clustering of Texts: Synthesis and Comparative Study

Bio-Inspired Techniques in the Clustering of Texts: Synthesis and Comparative Study

Reda Mohamed Hamou, Hadj Ahmed Bouarara, Abdelmalek Amine
Copyright: © 2015 |Volume: 6 |Issue: 4 |Pages: 30
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466677944|DOI: 10.4018/IJAMC.2015100103
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MLA

Hamou, Reda Mohamed, et al. "Bio-Inspired Techniques in the Clustering of Texts: Synthesis and Comparative Study." IJAMC vol.6, no.4 2015: pp.39-68. http://doi.org/10.4018/IJAMC.2015100103

APA

Hamou, R. M., Bouarara, H. A., & Amine, A. (2015). Bio-Inspired Techniques in the Clustering of Texts: Synthesis and Comparative Study. International Journal of Applied Metaheuristic Computing (IJAMC), 6(4), 39-68. http://doi.org/10.4018/IJAMC.2015100103

Chicago

Hamou, Reda Mohamed, Hadj Ahmed Bouarara, and Abdelmalek Amine. "Bio-Inspired Techniques in the Clustering of Texts: Synthesis and Comparative Study," International Journal of Applied Metaheuristic Computing (IJAMC) 6, no.4: 39-68. http://doi.org/10.4018/IJAMC.2015100103

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Abstract

Today, the development of a large scale access network internet/intranet has increased the amount of textual information available online/offline, where billions of documents have been created. In the last few years, bio inspired techniques which invaded the world of text-mining such, as clustering, represents a critical problem in the digital society especially over the world of information retrieval (IR). The content of this article is a recapitulation of a set of works as a comparative study between the authors' experiments realized by applying a set of bio-inspired techniques (social spiders(SS), 2D Cellular automata (2D-CA), 3D cellular automata (3D-CA), Artificial immune system (AIS), Particle swarm optimization (PSO)) and other techniques founded in literature (Ants Colony Optimization (ACO) and Genetic algorithms (GAs)) for solving the text clustering challenge by using the benchmark Reuter 21785. They analyse the different results in term of entropy, f-measure, execution time, and clusters number in order to find the ideal configuration (similarity measure and text representation method) for each technique. Their objectives are to improve the efficiency of text clustering systems and make decisions that can be the starting point for other researchers.

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