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Veuillez utiliser cette adresse pour citer ce document : http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/2913

Titre: Improved Cuckoo Search Algorithm for Document Clustering
Auteur(s): Ishak Boushak, Saida
Kamel, Nadjet
Bendjeghaba, Omar
Mots-clés: Document clustering
Vector space model
Cuckoo search
Cosine similarity
F-measure
Purity
Metaheuristic
Date de publication: 2015
Editeur: SPRINGER
Référence bibliographique: 5th IFIP TC 5 International Conference on Computer Science and Its Applications (CIA), Tahar Moulay Univ Saida, Saida, ALGERIA, MAY 20-21, 2015
Collection/Numéro: COMPUTER SCIENCE AND ITS APPLICATIONS, CIIA 2015, IFIP Advances in Information and Communication Technology;Vol 456 , pp. 217-228
Résumé: Efficient document clustering plays an important role in organizing and browsing the information in the World Wide Web. K-means is the most popular clustering algorithms, due to its simplicity and efficiency. However, it may be trapped in local minimum which leads to poor results. Recently, cuckoo search based clustering has proved to reach interesting results. By against, the number of iterations can increase dramatically due to its slowness convergence. In this paper, we propose an improved cuckoo search clustering algorithm in order to overcome the weakness of the conventional cuckoo search clustering. In this algorithm, the global search procedure is enhanced by a local search method. The experiments tests on four text document datasets and one standard dataset extracted from well known collections show the effectiveness and the robustness of the proposed algorithm to improve significantly the clustering quality in term of fitness function, f-measure and purity.
URI/URL: http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/2913
ISBN: 978-3-319-19578-0
978-3-319-19577-3
ISSN: 1868-4238
Collection(s) :Communications Internationales

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