The recursive scheme of clustering
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866929206467756032 |
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| author | Miniak-Górecka, Alicja Podlaski, Krzysztof Gwizdałła, Tomasz |
| author_facet | Miniak-Górecka, Alicja Podlaski, Krzysztof Gwizdałła, Tomasz |
| contents | The problem of data clustering is one of the most important in data analysis. It can be problematic when dealing with experimental data characterized by measurement uncertainties and errors. Our paper proposes a recursive scheme for clustering data obtained in geographical (climatological) experiments. The discussion of results obtained by k-means and SOM methods with the developed recursive procedure is presented. We show that the clustering using the new approach gives more acceptable results when compared to experts assessments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05479 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The recursive scheme of clustering Miniak-Górecka, Alicja Podlaski, Krzysztof Gwizdałła, Tomasz Machine Learning The problem of data clustering is one of the most important in data analysis. It can be problematic when dealing with experimental data characterized by measurement uncertainties and errors. Our paper proposes a recursive scheme for clustering data obtained in geographical (climatological) experiments. The discussion of results obtained by k-means and SOM methods with the developed recursive procedure is presented. We show that the clustering using the new approach gives more acceptable results when compared to experts assessments. |
| title | The recursive scheme of clustering |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.05479 |