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:: Volume 16, Issue 1 (9-2022) ::
JSS 2022, 16(1): 239-252 Back to browse issues page
Comparison of Clustering High Dimensional Data by Random Projections Method and Some Common Methods of Dimensional Reduction
Mousa Golalizadeh * , Sedigheh Noorani
Abstract:   (2725 Views)
Nowadays, the observations in many scientific fields, including biological sciences, are often high dimensional, meaning the number of variables exceeds the number of samples. One of the problems in model-based clustering of these data types is the estimation of too many parameters. To overcome this problem, the dimension of data must be first reduced before clustering, which can be done through dimension reduction methods. In this context, a recent approach that is recently receiving more attention is the random Projections method. This method has been studied from theoretical and practical perspectives in this paper. Its superiority over some conventional approaches such as principal component analysis and variable selection method was shown in analyzing three real data sets.
Keywords: High Dimensional Data, Model-Based Clustering, Dimension Reduction Methods, Random Projections.
Full-Text [PDF 250 kb]   (1387 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2021/10/26 | Accepted: 2022/09/1 | Published: 2022/08/2
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Golalizadeh M, Noorani S. Comparison of Clustering High Dimensional Data by Random Projections Method and Some Common Methods of Dimensional Reduction. JSS 2022; 16 (1) :239-252
URL: http://jss.irstat.ir/article-1-785-en.html

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 16, Issue 1 (9-2022) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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