کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6372146 1319967 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A biased random-key genetic algorithm for data clustering
ترجمه فارسی عنوان
یک الگوریتم ژنتیک تصادفی کلید انتخاب شده برای خوشه بندی داده
کلمات کلیدی
زیست شناسی محاسباتی، پیش بینی ساختار مولکولی، خوشه بندی بهینه سازی ترکیبی،
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک علوم کشاورزی و بیولوژیک (عمومی)
چکیده انگلیسی

Cluster analysis aims at finding subsets (clusters) of a given set of entities, which are homogeneous and/or well separated.Starting from the 1990s, cluster analysis has been applied to several domains with numerous applications. It has emerged as one of the most exciting interdisciplinary fields, having benefited from concepts and theoretical results obtained by different scientific research communities, including genetics, biology, biochemistry, mathematics, and computer science.The last decade has brought several new algorithms, which are able to solve larger sized and real-world instances. We will give an overview of the main types of clustering and criteria for homogeneity or separation. Solution techniques are discussed, with special emphasis on the combinatorial optimization perspective, with the goal of providing conceptual insights and literature references to the broad community of clustering practitioners.A new biased random-key genetic algorithm is also described and compared with several efficient hybrid GRASP algorithms recently proposed to cluster biological data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mathematical Biosciences - Volume 245, Issue 1, September 2013, Pages 76-85
نویسندگان
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