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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2401.15773 |
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| _version_ | 1866909085266345984 |
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| author | Lee, Ming-Chang Lin, Jia-Chun Stolz, Volker |
| author_facet | Lee, Ming-Chang Lin, Jia-Chun Stolz, Volker |
| contents | Despite the widespread use of k-means time series clustering in various domains, there exists a gap in the literature regarding its comprehensive evaluation with different time series normalization approaches. This paper seeks to fill this gap by conducting a thorough performance evaluation of k-means time series clustering on real-world open-source time series datasets. The evaluation focuses on two distinct normalization techniques: z-normalization and NP-Free. The former is one of the most commonly used normalization approach for time series. The latter is a real-time time series representation approach, which can serve as a time series normalization approach. The primary objective of this paper is to assess the impact of these two normalization techniques on k-means time series clustering in terms of its clustering quality. The experiments employ the silhouette score, a well-established metric for evaluating the quality of clusters in a dataset. By systematically investigating the performance of k-means time series clustering with these two normalization techniques, this paper addresses the current gap in k-means time series clustering evaluation and contributes valuable insights to the development of time series clustering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15773 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluation of k-means time series clustering based on z-normalization and NP-Free Lee, Ming-Chang Lin, Jia-Chun Stolz, Volker Machine Learning Artificial Intelligence Despite the widespread use of k-means time series clustering in various domains, there exists a gap in the literature regarding its comprehensive evaluation with different time series normalization approaches. This paper seeks to fill this gap by conducting a thorough performance evaluation of k-means time series clustering on real-world open-source time series datasets. The evaluation focuses on two distinct normalization techniques: z-normalization and NP-Free. The former is one of the most commonly used normalization approach for time series. The latter is a real-time time series representation approach, which can serve as a time series normalization approach. The primary objective of this paper is to assess the impact of these two normalization techniques on k-means time series clustering in terms of its clustering quality. The experiments employ the silhouette score, a well-established metric for evaluating the quality of clusters in a dataset. By systematically investigating the performance of k-means time series clustering with these two normalization techniques, this paper addresses the current gap in k-means time series clustering evaluation and contributes valuable insights to the development of time series clustering. |
| title | Evaluation of k-means time series clustering based on z-normalization and NP-Free |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.15773 |