Communications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11

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Author: search.filters.author.Bendjeghaba, OmarSubject: search.filters.subject.Optimization

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    Document clustering analysis based on hybrid cuckoo search and K-means algorithm
    (IEEE, 2021) Boushaki, Saida Ishak; Bendjeghaba, Omar; Brakta, Noureddine
    The clustering is an interesting technique for unsupervised document organization in the World Wide Web (WWW). The most widely used partitioning clustering algorithm is K-means. However, it has an issue with random initialization, which might lead to local optimum situations. In fact, metaheuristics-based clustering has demonstrated their efficiency to reach a global solution instead of local one. The Cuckoo search (CS) has been widely used for the clustering problem. However, the number of iterations grows dramatically when the dataset is high dimensional like the documents. In this study, the hybridization cuckoo search and K-means algorithms for the document clustering are analyzed. So, three hybrid algorithms are investigated and compared. The performance and the efficiency of the proposed algorithms are evaluated using Reuters 21578 Text Categorization Benchmark Dataset. The obtained results show the capability of the new approaches to generate more compact clustering and enhancing purity and F-measure clustering qualities
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    Accelerated modified sine cosine algorithm for data clustering
    (IEEE, 2021) Boushaki, Saida Ishak; Bendjeghaba, Omar; Brakta, Noureddine
    In artificial intelligence, data mining is a process that automatically discover valuable information from huge amounts of data in order to obtain knowledge. The most important unsupervised technique of data mining is the clustering technic, which his main task is dividing the dataset into homogeneous groups. Metaheuristics based clustering is an actual research area where optimization algorithms have demonstrated their efficiencies to provide near optimal solutions to this problem in a reasonable time, including the recent Sine Cosine metaheuristic Algorithm (SCA). However, its convergence rate is still rather slow. In this paper, an upgraded adaptation of SCA is proposed to improve the exhibition capacities of the quest strategy for ideal results for data Clustering problem, named AMSCAC. In this algorithm, both the local and global search procedures are enhanced by additional strategy. The experimental results on five standard datasets are promising and confirm the superiority of AMSCAC, for the clustering results over SCA, cuckoo search algorithm (CS), differential evolution algorithm (DE), and genetic algorithm (GA)
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    Improved cuckoo search algorithm for document clustering
    (Springer, 2015) Boushaki, Saida Ishak; Kamel, Nadjet; Bendjeghaba, Omar