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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.05945 |
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| _version_ | 1866915518894571520 |
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| author | Tsagris, Michail Kontemeniotis, Nikolaos |
| author_facet | Tsagris, Michail Kontemeniotis, Nikolaos |
| contents | We introduce two simplicial clustering approaches for compositional data, that are adaptations of the $K$--means and of the Gaussian mixture models algorithms, by employing the $α$--transformation. By utilizing clustering validation indices we can decide on the number of clusters and choose the value of $α$ for the $K$--means, while for the model-based clustering approach information criteria complete this task. extensive simulation studies compare the performance of these two approaches and a real data set illustrates their performance in real world settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simplicial clustering using the $α$--transformation Tsagris, Michail Kontemeniotis, Nikolaos Methodology We introduce two simplicial clustering approaches for compositional data, that are adaptations of the $K$--means and of the Gaussian mixture models algorithms, by employing the $α$--transformation. By utilizing clustering validation indices we can decide on the number of clusters and choose the value of $α$ for the $K$--means, while for the model-based clustering approach information criteria complete this task. extensive simulation studies compare the performance of these two approaches and a real data set illustrates their performance in real world settings. |
| title | Simplicial clustering using the $α$--transformation |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.05945 |