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Comparison of Clustering High Dimensional Data by Random Projections Method and Some Common Methods of Dimensional Reduction
Mousa Golalizadeh *, Sedigheh Noorani
Abstract:   (659 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 3985 kb]   (273 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2021/10/26 | Accepted: 2022/09/1
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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