دانلود مقاله ISI انگلیسی شماره 150650
ترجمه فارسی عنوان مقاله

الگوریتم بهینه سازی عنکبوت اجتماعی مبتنی بر روش ساده برای تجزیه و تحلیل خوشه ای

عنوان انگلیسی
A simplex method-based social spider optimization algorithm for clustering analysis
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
150650 2017 16 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Engineering Applications of Artificial Intelligence, Volume 64, September 2017, Pages 67-82

ترجمه کلمات کلیدی
تجزیه خوشه ای، الگوریتم بهینه سازی اجتماعی عنکبوتی، روش ساده، مجموعه داده های معیار، الگوریتم فراشناسی،
کلمات کلیدی انگلیسی
Clustering analysis; Social-spider optimization algorithm; Simplex method; Benchmark datasets; Meta-heuristic algorithm;
پیش نمایش مقاله
پیش نمایش مقاله  الگوریتم بهینه سازی عنکبوت اجتماعی مبتنی بر روش ساده برای تجزیه و تحلیل خوشه ای

چکیده انگلیسی

Clustering is a popular data-analysis and data-mining technique that has been addressed in many contexts and by researchers in many disciplines. The K-means algorithm is one of the most popular clustering algorithms because of its simplicity and easiness in application. However, its performance depends strongly on the initial cluster centers used and can converge to local minima. To overcome these problems, many scholars have attempted to solve the clustering problem using meta-heuristic algorithms. However, as the dimensionality of a search space and the data contained within it increase, the problem of local optima entrapment and poor convergence rates persist; even the efficiency and effectiveness of these algorithms are often unacceptable. This study presents a simplex method-based social spider optimization (SMSSO) algorithm to overcome the drawbacks mentioned above. The simplex method is a stochastic variant strategy that increases the diversity of a population while enhancing the local search ability of the algorithm. The application of the proposed algorithm on a data-clustering problem using eleven benchmark datasets confirms the potential and effectiveness of the proposed algorithm. The experimental results compared to the K-means technique and other state-of-the-art algorithms show that the SMSSO algorithm outperforms the other algorithms in terms of accuracy, robustness, and convergence speed.