ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915787760992256 |
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| author | Bakhtyari, Mohammadreza Mazoure, Bogdan de Amorim, Renato Cordeiro Rabusseau, Guillaume Makarenkov, Vladimir |
| author_facet | Bakhtyari, Mohammadreza Mazoure, Bogdan de Amorim, Renato Cordeiro Rabusseau, Guillaume Makarenkov, Vladimir |
| contents | In unsupervised learning, identifying an effective clustering algorithm for a given tabular dataset remains a fundamental challenge. We introduce ClustRecNet, a novel end-to-end deep learning framework that recommends a suitable clustering algorithm by directly learning high-order representations of raw tabular data. To facilitate robust meta-learning, we construct a comprehensive repository of 34,000 synthetic datasets with diverse structures, run 10 prominent clustering algorithms, and use Adjusted Rand Index (ARI) to establish ground-truth labels. ClustRecNet integrates convolutional, residual, and attention mechanisms to capture both local/global structural patterns, effectively bypassing the knowledge bottleneck associated with manual feature engineering. Extensive evaluations on both synthetic and real-world benchmarks demonstrate that ClustRecNet consistently outperforms state-of-the-art Automated Machine Learning (AutoML) approaches, including ML2DAC and AutoML4Clust. Our framework achieves an average 0.497 ARI gain over the well-known Calinski-Harabasz cluster validity index on synthetic data and an average 15.3% ARI improvement over the leading AutoML approach (ML2DAC) on real-world benchmarks. To the best of our knowledge, we are the first to successively apply deep learning to automatically recommend suitable clustering algorithms for tabular data at hand. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25289 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation Bakhtyari, Mohammadreza Mazoure, Bogdan de Amorim, Renato Cordeiro Rabusseau, Guillaume Makarenkov, Vladimir Machine Learning Artificial Intelligence In unsupervised learning, identifying an effective clustering algorithm for a given tabular dataset remains a fundamental challenge. We introduce ClustRecNet, a novel end-to-end deep learning framework that recommends a suitable clustering algorithm by directly learning high-order representations of raw tabular data. To facilitate robust meta-learning, we construct a comprehensive repository of 34,000 synthetic datasets with diverse structures, run 10 prominent clustering algorithms, and use Adjusted Rand Index (ARI) to establish ground-truth labels. ClustRecNet integrates convolutional, residual, and attention mechanisms to capture both local/global structural patterns, effectively bypassing the knowledge bottleneck associated with manual feature engineering. Extensive evaluations on both synthetic and real-world benchmarks demonstrate that ClustRecNet consistently outperforms state-of-the-art Automated Machine Learning (AutoML) approaches, including ML2DAC and AutoML4Clust. Our framework achieves an average 0.497 ARI gain over the well-known Calinski-Harabasz cluster validity index on synthetic data and an average 15.3% ARI improvement over the leading AutoML approach (ML2DAC) on real-world benchmarks. To the best of our knowledge, we are the first to successively apply deep learning to automatically recommend suitable clustering algorithms for tabular data at hand. |
| title | ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.25289 |