کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6903228 | 1446988 | 2018 | 43 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Continuous greedy randomized adaptive search procedure for data clustering
ترجمه فارسی عنوان
روش جستجوی انطباق تصادفی حریم خصوصی مستمر برای خوشه بندی داده ها
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Cluster analysis is an unsupervised machine learning task that aims at finding the most similar groups of objects, given a prespecified similarity measure. When modeled as an optimization problem, clustering problems generally are NP-hard. Therefore, the use of metaheuristic approaches appears to be a promising alternative. In this paper, a continuous greedy randomized adaptive search procedure (C-GRASP) approach is proposed to solve a partitional clustering problem that aims at minimizing the intra-cluster distances. Computational experiments carried out on existing databases showed that the results obtained by the proposed algorithm was, on average, superior to those found by other well-known metaheuristics, as well as to those achieved by state-of-the-art algorithms from the literature.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 72, November 2018, Pages 43-55
Journal: Applied Soft Computing - Volume 72, November 2018, Pages 43-55
نویسندگان
Eduardo Queiroga, Anand Subramanian, LucÃdio dos Anjos F. Cabral,