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An Optimal Configuration of Sensitive Parameters of PSO Applied to Textual Clustering
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Author(s): Reda Mohamed Hamou (Dr. Moulay Tahar University of Saida, Algeria), Abdelmalek Amine (GeCoDe Laboratory, Department of Computer Sciences, Dr. Tahar Moulay University of Saida, Algeria), Mohamed Amine Boudia (Dr. Tahar Moulay University of Saida, Algeria)and Ahmed Chaouki Lokbani (Dr. Tahar Moulay University of Saida, Algeria)
Copyright: 2019
Pages: 19
Source title:
Exploring Critical Approaches of Evolutionary Computation
Source Author(s)/Editor(s): Muhammad Sarfraz (Kuwait University, Kuwait)
DOI: 10.4018/978-1-5225-5832-3.ch010
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Abstract
The clustering aims to minimize intra-class distance in the cluster and maximize extra-classes distances between clusters. The text clustering is a very hard task; it is solved generally by metaheuristic. The current literature offers two major metaheuristic approaches: neighborhood metaheuristics and population metaheuristics. In this chapter, the authors seek to find the optimal configuration of sensitive parameters of the PSO algorithm applied to textual clustering. The study will go through in dissociable steps, namely the representation and indexing textual documents, clustering by biomimetic approach, optimized by PSO, the study of parameter sensitivity of the optimization technique, and improvement of clustering. The authors will test several parameters and keep the best configurations that return the best results of clustering. They will use the most widely used evaluation measures like index of Davies and Bouldin (internal) and two external: the F-measure and entropy, which are based on recall and precision.
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