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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.04269 |
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| _version_ | 1866916531765510144 |
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| author | Lin, Zixin Zulkepli, Nur Fariha Syaqina Kasihmuddin, Mohd Shareduwan Mohd Gobithaasan, R. U. |
| author_facet | Lin, Zixin Zulkepli, Nur Fariha Syaqina Kasihmuddin, Mohd Shareduwan Mohd Gobithaasan, R. U. |
| contents | The time-series data of sea level rise and fall contains crucial information on the variability of sea level patterns. Traditional $k$-means clustering is commonly used for categorizing regional variability of sea level, however, its results are not robust against a number of factors. This study analyzed fourteen datasets of monthly sea level in fourteen shoreline regions of Peninsular Malaysia. We applied a hybridization of clustering technique to analyze data categorization and topological data analysis method to enhance the performance of our clustering analysis. Specifically, our approach utilized the persistent homology and $k$-means/$k$-means++ clustering. The fourteen data sets from fourteen tide gauge stations were categorized in classes based on a prior categorization that was determined by topological information, and the probability of data points that belong to certain groups that is yielded by $k$-means/$k$-means++ clustering. Our results demonstrated that our method significantly improves the performance of traditional clustering techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04269 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Analysis of Sea Level Spatial Variability by Topological Indicators and $k$-means Clustering Algorithm Lin, Zixin Zulkepli, Nur Fariha Syaqina Kasihmuddin, Mohd Shareduwan Mohd Gobithaasan, R. U. Applications The time-series data of sea level rise and fall contains crucial information on the variability of sea level patterns. Traditional $k$-means clustering is commonly used for categorizing regional variability of sea level, however, its results are not robust against a number of factors. This study analyzed fourteen datasets of monthly sea level in fourteen shoreline regions of Peninsular Malaysia. We applied a hybridization of clustering technique to analyze data categorization and topological data analysis method to enhance the performance of our clustering analysis. Specifically, our approach utilized the persistent homology and $k$-means/$k$-means++ clustering. The fourteen data sets from fourteen tide gauge stations were categorized in classes based on a prior categorization that was determined by topological information, and the probability of data points that belong to certain groups that is yielded by $k$-means/$k$-means++ clustering. Our results demonstrated that our method significantly improves the performance of traditional clustering techniques. |
| title | An Analysis of Sea Level Spatial Variability by Topological Indicators and $k$-means Clustering Algorithm |
| topic | Applications |
| url | https://arxiv.org/abs/2405.04269 |