Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach
Determining the right number of clusters without any prior information about their numbers is a core problem in cluster analysis. In this paper, we propose a nonparametric clustering method based on different weighted spatial rank (WSR) functions. The main idea behind WSR is to define a dissimilarit...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2023/8849404 |
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| _version_ | 1850158548145668096 |
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| author | Mohammed H. Baragilly Hend Gabr Brian H. Willis |
| author_facet | Mohammed H. Baragilly Hend Gabr Brian H. Willis |
| author_sort | Mohammed H. Baragilly |
| collection | DOAJ |
| description | Determining the right number of clusters without any prior information about their numbers is a core problem in cluster analysis. In this paper, we propose a nonparametric clustering method based on different weighted spatial rank (WSR) functions. The main idea behind WSR is to define a dissimilarity measure locally based on a localized version of multivariate ranks. We consider a nonparametric Gaussian kernel weights function. We compare the performance of the method with other standard techniques and assess its misclassification rate. The method is completely data-driven, robust against distributional assumptions, and accurate for the purpose of intuitive visualization and can be used both to determine the number of clusters and assign each observation to its cluster. |
| format | Article |
| id | doaj-art-681bdaf8118f479b92e00a56250e988a |
| institution | OA Journals |
| issn | 1687-9538 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-681bdaf8118f479b92e00a56250e988a2025-08-20T02:23:50ZengWileyJournal of Probability and Statistics1687-95382023-01-01202310.1155/2023/8849404Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based ApproachMohammed H. Baragilly0Hend Gabr1Brian H. Willis2Department of MathematicsDepartment of MathematicsInstitute of Applied Health ResearchDetermining the right number of clusters without any prior information about their numbers is a core problem in cluster analysis. In this paper, we propose a nonparametric clustering method based on different weighted spatial rank (WSR) functions. The main idea behind WSR is to define a dissimilarity measure locally based on a localized version of multivariate ranks. We consider a nonparametric Gaussian kernel weights function. We compare the performance of the method with other standard techniques and assess its misclassification rate. The method is completely data-driven, robust against distributional assumptions, and accurate for the purpose of intuitive visualization and can be used both to determine the number of clusters and assign each observation to its cluster.http://dx.doi.org/10.1155/2023/8849404 |
| spellingShingle | Mohammed H. Baragilly Hend Gabr Brian H. Willis Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach Journal of Probability and Statistics |
| title | Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach |
| title_full | Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach |
| title_fullStr | Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach |
| title_full_unstemmed | Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach |
| title_short | Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach |
| title_sort | clustering analysis of multivariate data a weighted spatial ranks based approach |
| url | http://dx.doi.org/10.1155/2023/8849404 |
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