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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.23942 |
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| _version_ | 1866915520151814144 |
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| author | Daras, John N. |
| author_facet | Daras, John N. |
| contents | Advancements in tools like Shapely 2.0 and Triton can significantly improve the efficiency of spatial similarity computations by enabling faster and more scalable geometric operations. However, for extremely large datasets, these optimizations may face challenges due to the sheer volume of computations required. To address this, we propose a framework that reduces the number of clusters requiring verification, thereby decreasing the computational load on these systems. The framework integrates dynamic similarity index thresholding, supervised scheduling, and recall-constrained optimization to efficiently identify clusters with the highest spatial similarity while meeting user-defined precision and recall requirements. By leveraging Kernel Density Estimation (KDE) to dynamically determine similarity thresholds and machine learning models to prioritize clusters, our approach achieves substantial reductions in computational cost without sacrificing accuracy. Experimental results demonstrate the scalability and effectiveness of the method, offering a practical solution for large-scale geospatial analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23942 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Identification of High Similarity Clusters in Polygon Datasets Daras, John N. Machine Learning Databases Quantitative Methods Advancements in tools like Shapely 2.0 and Triton can significantly improve the efficiency of spatial similarity computations by enabling faster and more scalable geometric operations. However, for extremely large datasets, these optimizations may face challenges due to the sheer volume of computations required. To address this, we propose a framework that reduces the number of clusters requiring verification, thereby decreasing the computational load on these systems. The framework integrates dynamic similarity index thresholding, supervised scheduling, and recall-constrained optimization to efficiently identify clusters with the highest spatial similarity while meeting user-defined precision and recall requirements. By leveraging Kernel Density Estimation (KDE) to dynamically determine similarity thresholds and machine learning models to prioritize clusters, our approach achieves substantial reductions in computational cost without sacrificing accuracy. Experimental results demonstrate the scalability and effectiveness of the method, offering a practical solution for large-scale geospatial analysis. |
| title | Efficient Identification of High Similarity Clusters in Polygon Datasets |
| topic | Machine Learning Databases Quantitative Methods |
| url | https://arxiv.org/abs/2509.23942 |