Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918220243402752 |
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| author | Hashemian, Sajjad |
| author_facet | Hashemian, Sajjad |
| contents | In this paper, we propose a method for density-based clustering in high-dimensional spaces that combines Locality-Sensitive Hashing (LSH) with the Quick Shift algorithm. The Quick Shift algorithm, known for its hierarchical clustering capabilities, is extended by integrating approximate Kernel Density Estimation (KDE) using LSH to provide efficient density estimates. The proposed approach achieves almost linear time complexity while preserving the consistency of density-based clustering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering Hashemian, Sajjad Machine Learning I.5.3; I.2.10 In this paper, we propose a method for density-based clustering in high-dimensional spaces that combines Locality-Sensitive Hashing (LSH) with the Quick Shift algorithm. The Quick Shift algorithm, known for its hierarchical clustering capabilities, is extended by integrating approximate Kernel Density Estimation (KDE) using LSH to provide efficient density estimates. The proposed approach achieves almost linear time complexity while preserving the consistency of density-based clustering. |
| title | Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering |
| topic | Machine Learning I.5.3; I.2.10 |
| url | https://arxiv.org/abs/2503.07995 |