کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6883438 | 1444172 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A comparative study of clustering ensemble algorithms
ترجمه فارسی عنوان
مطالعه تطبیقی الگوریتم های گروه بندی خوشه ای
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کلمات کلیدی
گروه خوشه بندی مکانیسم تولید، تابع توافق، عضو گروه تنوع اندازه گروه،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی
Since clustering ensemble was proposed, it has rapidly attracted much attention. This paper makes an overview of recent research on clustering ensemble about generative mechanism, selective clustering ensemble, consensus function and application. Twelve clustering ensemble algorithms are described and compared to choose a basic one. The experiment shows that using k-means with different initializations as generative mechanism and average-linkage agglomerative clustering as consensus function is the best one. As ensemble size increases, the performance of clustering ensemble improves. The basic clustering ensemble algorithm with suitable ensemble size is compared with clustering algorithms and the experiment shows that clustering ensemble is better than clustering. The influence of diversity on clustering ensemble is instructive to selecting members. The experiment shows that selecting members in high quality and big diversity for low-dimensional data sets, and selecting members in high quality and median diversity for high-dimensional data sets are better than traditional clustering ensemble.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Electrical Engineering - Volume 68, May 2018, Pages 603-615
Journal: Computers & Electrical Engineering - Volume 68, May 2018, Pages 603-615
نویسندگان
Xiuge Wu, Tinghuai Ma, Jie Cao, Yuan Tian, Alia Alabdulkarim,