The advantages of k-visibility: A comparative analysis of several time series clustering algorithms
This paper outlined the advantages of the k-visibility algorithm proposed in [1,2] compared to traditional time series clustering algorithms, highlighting enhanced computational efficiency and comparable clustering quality. This method leveraged visibility graphs, transforming time series into graph...
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| Language: | English |
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AIMS Press
2024-12-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20241687 |
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| author | Sergio Iglesias-Perez Alberto Partida Regino Criado |
| author_facet | Sergio Iglesias-Perez Alberto Partida Regino Criado |
| author_sort | Sergio Iglesias-Perez |
| collection | DOAJ |
| description | This paper outlined the advantages of the k-visibility algorithm proposed in [1,2] compared to traditional time series clustering algorithms, highlighting enhanced computational efficiency and comparable clustering quality. This method leveraged visibility graphs, transforming time series into graph structures where data points were represented as nodes, and edges are established based on visibility criteria. It employed the traditional k-means clustering method to cluster the time series. This approach was particularly efficient for long time series and demonstrated superior performance compared to existing clustering methods. The structural properties of visibility graphs provided a robust foundation for clustering, effectively capturing both local and global patterns within the data. In this paper, we have compared the k-visibility algorithm with 4 algorithms frequently used in time series clustering and compared the results in terms of accuracy and computational time. To validate the results, we have selected 15 datasets from the prestigious UCR (University of California, Riverside) archive in order to make a homogeneous validation. The result of this comparison concluded that k-visibility was always the fastest algorithm and that it was one of the most accurate in matching the clustering proposed by the UCR archive. |
| format | Article |
| id | doaj-art-db2633a3c8be48e390ff97373b426cbd |
| institution | Kabale University |
| issn | 2473-6988 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj-art-db2633a3c8be48e390ff97373b426cbd2025-01-23T07:53:25ZengAIMS PressAIMS Mathematics2473-69882024-12-01912355513556910.3934/math.20241687The advantages of k-visibility: A comparative analysis of several time series clustering algorithmsSergio Iglesias-Perez0Alberto Partida1Regino Criado2Data, Complex Networks and Cybersecurity Sciences Technological Institute, Univ. Rey Juan Carlos, 28028 Madrid, SpainData, Complex Networks and Cybersecurity Sciences Technological Institute, Univ. Rey Juan Carlos, 28028 Madrid, SpainData, Complex Networks and Cybersecurity Sciences Technological Institute, Univ. Rey Juan Carlos, 28028 Madrid, SpainThis paper outlined the advantages of the k-visibility algorithm proposed in [1,2] compared to traditional time series clustering algorithms, highlighting enhanced computational efficiency and comparable clustering quality. This method leveraged visibility graphs, transforming time series into graph structures where data points were represented as nodes, and edges are established based on visibility criteria. It employed the traditional k-means clustering method to cluster the time series. This approach was particularly efficient for long time series and demonstrated superior performance compared to existing clustering methods. The structural properties of visibility graphs provided a robust foundation for clustering, effectively capturing both local and global patterns within the data. In this paper, we have compared the k-visibility algorithm with 4 algorithms frequently used in time series clustering and compared the results in terms of accuracy and computational time. To validate the results, we have selected 15 datasets from the prestigious UCR (University of California, Riverside) archive in order to make a homogeneous validation. The result of this comparison concluded that k-visibility was always the fastest algorithm and that it was one of the most accurate in matching the clustering proposed by the UCR archive.https://www.aimspress.com/article/doi/10.3934/math.20241687time seriesvisibility graphclusteringcomputational efficiency |
| spellingShingle | Sergio Iglesias-Perez Alberto Partida Regino Criado The advantages of k-visibility: A comparative analysis of several time series clustering algorithms AIMS Mathematics time series visibility graph clustering computational efficiency |
| title | The advantages of k-visibility: A comparative analysis of several time series clustering algorithms |
| title_full | The advantages of k-visibility: A comparative analysis of several time series clustering algorithms |
| title_fullStr | The advantages of k-visibility: A comparative analysis of several time series clustering algorithms |
| title_full_unstemmed | The advantages of k-visibility: A comparative analysis of several time series clustering algorithms |
| title_short | The advantages of k-visibility: A comparative analysis of several time series clustering algorithms |
| title_sort | advantages of k visibility a comparative analysis of several time series clustering algorithms |
| topic | time series visibility graph clustering computational efficiency |
| url | https://www.aimspress.com/article/doi/10.3934/math.20241687 |
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