Equivalence partition based morphological similarity clustering for large-scale time series
Abstract Data clustering belongs to the category of unsupervised learning and plays an important role in the dynamic systems and big data. The clustering problem of sampled time-series data is undoubtedly much more challenging than that of repeatable sampling data. Most of the existing time-series c...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-33074-6 |
| _version_ | 1797845993603989504 |
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| author | Shaolin Hu |
| author_facet | Shaolin Hu |
| author_sort | Shaolin Hu |
| collection | DOAJ |
| description | Abstract Data clustering belongs to the category of unsupervised learning and plays an important role in the dynamic systems and big data. The clustering problem of sampled time-series data is undoubtedly much more challenging than that of repeatable sampling data. Most of the existing time-series clustering methods stay at the level of algorithm design, lacking rigorous theoretical foundation and being inefficient in dealing with large-scale time series. To address this issue, in this paper, we establish the mathematical theory for the large-scale time series clustering of dynamic system. The main contributions of this paper include proposing the concept of time series morphological isomorphism, proving that translation isomorphism and stretching isomorphism are equivalent relations, developing the calculation method of morphological similarity measure, and establishing a new time series clustering method based on equivalent partition and morphological similarity. These contributions provide a new theoretical foundation and practical method for the clustering of large-scale time series. Simulation results in typical applications verify the validity and practicability of the aforementioned clustering methods. |
| first_indexed | 2024-04-09T17:47:53Z |
| format | Article |
| id | doaj.art-1dce6fe0458d42f4b8023bf24dc70428 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T17:47:53Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1dce6fe0458d42f4b8023bf24dc704282023-04-16T11:12:00ZengNature PortfolioScientific Reports2045-23222023-04-0113111210.1038/s41598-023-33074-6Equivalence partition based morphological similarity clustering for large-scale time seriesShaolin Hu0Automation School, Guangdong University of Petrochemical TechnologyAbstract Data clustering belongs to the category of unsupervised learning and plays an important role in the dynamic systems and big data. The clustering problem of sampled time-series data is undoubtedly much more challenging than that of repeatable sampling data. Most of the existing time-series clustering methods stay at the level of algorithm design, lacking rigorous theoretical foundation and being inefficient in dealing with large-scale time series. To address this issue, in this paper, we establish the mathematical theory for the large-scale time series clustering of dynamic system. The main contributions of this paper include proposing the concept of time series morphological isomorphism, proving that translation isomorphism and stretching isomorphism are equivalent relations, developing the calculation method of morphological similarity measure, and establishing a new time series clustering method based on equivalent partition and morphological similarity. These contributions provide a new theoretical foundation and practical method for the clustering of large-scale time series. Simulation results in typical applications verify the validity and practicability of the aforementioned clustering methods.https://doi.org/10.1038/s41598-023-33074-6 |
| spellingShingle | Shaolin Hu Equivalence partition based morphological similarity clustering for large-scale time series Scientific Reports |
| title | Equivalence partition based morphological similarity clustering for large-scale time series |
| title_full | Equivalence partition based morphological similarity clustering for large-scale time series |
| title_fullStr | Equivalence partition based morphological similarity clustering for large-scale time series |
| title_full_unstemmed | Equivalence partition based morphological similarity clustering for large-scale time series |
| title_short | Equivalence partition based morphological similarity clustering for large-scale time series |
| title_sort | equivalence partition based morphological similarity clustering for large scale time series |
| url | https://doi.org/10.1038/s41598-023-33074-6 |
| work_keys_str_mv | AT shaolinhu equivalencepartitionbasedmorphologicalsimilarityclusteringforlargescaletimeseries |