Clustering Change Sign Detection by Fusing Mixture Complexity
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911816236400640 |
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| author | Urano, Kento Yuki, Ryo Yamanishi, Kenji |
| author_facet | Urano, Kento Yuki, Ryo Yamanishi, Kenji |
| contents | This paper proposes an early detection method for cluster structural changes. Cluster structure refers to discrete structural characteristics, such as the number of clusters, when data are represented using finite mixture models, such as Gaussian mixture models. We focused on scenarios in which the cluster structure gradually changed over time. For finite mixture models, the concept of mixture complexity (MC) measures the continuous cluster size by considering the cluster proportion bias and overlap between clusters. In this paper, we propose MC fusion as an extension of MC to handle situations in which multiple mixture numbers are possible in a finite mixture model. By incorporating the fusion of multiple models, our approach accurately captured the cluster structure during transitional periods of gradual change. Moreover, we introduce a method for detecting changes in the cluster structure by examining the transition of MC fusion. We demonstrate the effectiveness of our method through empirical analysis using both artificial and real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18269 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Clustering Change Sign Detection by Fusing Mixture Complexity Urano, Kento Yuki, Ryo Yamanishi, Kenji Machine Learning Information Theory This paper proposes an early detection method for cluster structural changes. Cluster structure refers to discrete structural characteristics, such as the number of clusters, when data are represented using finite mixture models, such as Gaussian mixture models. We focused on scenarios in which the cluster structure gradually changed over time. For finite mixture models, the concept of mixture complexity (MC) measures the continuous cluster size by considering the cluster proportion bias and overlap between clusters. In this paper, we propose MC fusion as an extension of MC to handle situations in which multiple mixture numbers are possible in a finite mixture model. By incorporating the fusion of multiple models, our approach accurately captured the cluster structure during transitional periods of gradual change. Moreover, we introduce a method for detecting changes in the cluster structure by examining the transition of MC fusion. We demonstrate the effectiveness of our method through empirical analysis using both artificial and real-world datasets. |
| title | Clustering Change Sign Detection by Fusing Mixture Complexity |
| topic | Machine Learning Information Theory |
| url | https://arxiv.org/abs/2403.18269 |