ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation

• URL: http://arxiv.org/abs/2509.25289v2
• Date: Fri, 10 Oct 2025 16:12:30 GMT
• Title: ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
• Authors: Mohammadreza Bakhtyari, Bogdan Mazoure, Renato Cordeiro de Amorim, Guillaume Rabusseau, Vladimir Makarenkov,
• Abstract summary: ClustRecNet is a novel deep learning (DL)-based recommendation framework for determining the most suitable clustering algorithms for a given dataset.<n>We construct a comprehensive data repository comprising 34,000 synthetic datasets with diverse structural properties.<n>The proposed network architecture integrates convolutional, residual, and attention mechanisms to capture both local and global structural patterns.
• Score: 9.419239935565376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce ClustRecNet - a novel deep learning (DL)-based recommendation framework for determining the most suitable clustering algorithms for a given dataset, addressing the long-standing challenge of clustering algorithm selection in unsupervised learning. To enable supervised learning in this context, we construct a comprehensive data repository comprising 34,000 synthetic datasets with diverse structural properties. Each of them was processed using 10 popular clustering algorithms. The resulting clusterings were assessed via the Adjusted Rand Index (ARI) to establish ground truth labels, used for training and evaluation of our DL model. The proposed network architecture integrates convolutional, residual, and attention mechanisms to capture both local and global structural patterns from the input data. This design supports end-to-end training to learn compact representations of datasets and enables direct recommendation of the most suitable clustering algorithm, reducing reliance on handcrafted meta-features and traditional Cluster Validity Indices (CVIs). Comprehensive experiments across synthetic and real-world benchmarks demonstrate that our DL model consistently outperforms conventional CVIs (e.g. Silhouette, Calinski-Harabasz, Davies-Bouldin, and Dunn) as well as state-of-the-art AutoML clustering recommendation approaches (e.g. ML2DAC, AutoCluster, and AutoML4Clust). Notably, the proposed model achieves a 0.497 ARI improvement over the Calinski-Harabasz index on synthetic data and a 15.3% ARI gain over the best-performing AutoML approach on real-world data.

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