SACA: Selective Attention-Based Clustering Algorithm
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914173070344192 |
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| author | Bilehsavar, Meysam Shirdel Ghaedi, Razieh Taheri, Samira Seyed Fan, Xinqi O'Reilly, Christian |
| author_facet | Bilehsavar, Meysam Shirdel Ghaedi, Razieh Taheri, Samira Seyed Fan, Xinqi O'Reilly, Christian |
| contents | Clustering algorithms are fundamental tools across many fields, with density-based methods offering particular advantages in identifying arbitrarily shaped clusters and handling noise. However, their effectiveness is often limited by the requirement of critical parameter tuning by users, which typically requires significant domain expertise. This paper introduces a novel density-based clustering algorithm loosely inspired by the concept of selective attention, designed to minimize reliance on parameter tuning for most applications. The proposed method computes an adaptive threshold to exclude sparsely distributed points and outliers, constructs an initial cluster framework, and subsequently reintegrates the filtered points to refine the final results. Extensive experiments on diverse benchmark datasets demonstrate the robustness, accuracy, and ease of use of the proposed approach, establishing it as a powerful alternative to conventional density-based clustering techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17150 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SACA: Selective Attention-Based Clustering Algorithm Bilehsavar, Meysam Shirdel Ghaedi, Razieh Taheri, Samira Seyed Fan, Xinqi O'Reilly, Christian Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition I.2; I.5 Clustering algorithms are fundamental tools across many fields, with density-based methods offering particular advantages in identifying arbitrarily shaped clusters and handling noise. However, their effectiveness is often limited by the requirement of critical parameter tuning by users, which typically requires significant domain expertise. This paper introduces a novel density-based clustering algorithm loosely inspired by the concept of selective attention, designed to minimize reliance on parameter tuning for most applications. The proposed method computes an adaptive threshold to exclude sparsely distributed points and outliers, constructs an initial cluster framework, and subsequently reintegrates the filtered points to refine the final results. Extensive experiments on diverse benchmark datasets demonstrate the robustness, accuracy, and ease of use of the proposed approach, establishing it as a powerful alternative to conventional density-based clustering techniques. |
| title | SACA: Selective Attention-Based Clustering Algorithm |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition I.2; I.5 |
| url | https://arxiv.org/abs/2508.17150 |