Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.09399 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914915498852352 |
|---|---|
| author | Raphael, Steven Shun, Julian |
| author_facet | Raphael, Steven Shun, Julian |
| contents | Filtered graphs provide a powerful tool for data clustering. The triangular maximally filtered graph (TMFG) method, when combined with the directed bubble hierarchy tree (DBHT) method, defines a useful algorithm for hierarchical data clustering. This combined TMFG-DBHT algorithm has been shown to produce clusters with good accuracy for time series data, but the previous state-of-the-art parallel algorithm has limited parallelism. This paper presents an improved parallel algorithm for TMFG-DBHT. Our algorithm increases the amount of parallelism by aggregating the bulk of the work of TMFG construction together to reduce the overheads of parallelism. Furthermore, our TMFG algorithm updates information lazily, which reduces the overall work. We find further speedups by computing all-pairs shortest paths approximately instead of exactly in DBHT. We show experimentally that our algorithm gives a 3.7--10.7x speedup over the previous state-of-the-art TMFG-DBHT implementation, while preserving clustering accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09399 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Faster Parallel Triangular Maximally Filtered Graphs and Hierarchical Clustering Raphael, Steven Shun, Julian Distributed, Parallel, and Cluster Computing Filtered graphs provide a powerful tool for data clustering. The triangular maximally filtered graph (TMFG) method, when combined with the directed bubble hierarchy tree (DBHT) method, defines a useful algorithm for hierarchical data clustering. This combined TMFG-DBHT algorithm has been shown to produce clusters with good accuracy for time series data, but the previous state-of-the-art parallel algorithm has limited parallelism. This paper presents an improved parallel algorithm for TMFG-DBHT. Our algorithm increases the amount of parallelism by aggregating the bulk of the work of TMFG construction together to reduce the overheads of parallelism. Furthermore, our TMFG algorithm updates information lazily, which reduces the overall work. We find further speedups by computing all-pairs shortest paths approximately instead of exactly in DBHT. We show experimentally that our algorithm gives a 3.7--10.7x speedup over the previous state-of-the-art TMFG-DBHT implementation, while preserving clustering accuracy. |
| title | Faster Parallel Triangular Maximally Filtered Graphs and Hierarchical Clustering |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2408.09399 |