Publications Scientifiques
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Item Accelerated modified sine cosine algorithm for data clustering(IEEE, 2021) Boushaki, Saida Ishak; Bendjeghaba, Omar; Brakta, NoureddineIn 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)Item Improved Cuckoo Search Algorithm for Document Clustering(SPRINGER, 2015) Ishak Boushak, Saida; Kamel, Nadjet; Bendjeghaba, OmarEfficient 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.Item Improved cuckoo search algorithm for document clustering(Springer, 2015) Boushaki, Saida Ishak; Kamel, Nadjet; Bendjeghaba, OmarItem A new hybrid algorithm for document clustering based on cuckoo search and K-means(Springer, 2014) Ishak Boushaki, Saida; Nadjet, Kamel; Bendjeghaba, OmarItem Ant colony system for reliability optimization design problem of multi-state power system(Institute of Thermomechanics AS CR, 2009) Bendjeghaba, O.; Ouahdi, D.Item Firefly algorithm for optimal tuning of PID controller parameters(IEEE, 2013) Bendjeghaba, O.; Boushaki, Saida Ishak; Zemmour, N.