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.

翻译：本文提出ClustRecNet——一种基于深度学习（DL）的新型推荐框架，旨在为给定数据集确定最合适的聚类算法，以解决无监督学习中长期存在的聚类算法选择难题。为在此背景下实现监督学习，我们构建了一个包含34,000个具有多样化结构特性的合成数据集的综合数据仓库。每个数据集均使用10种主流聚类算法进行处理，并通过调整兰德指数（ARI）对所得聚类结果进行评估，从而建立用于深度学习模型训练与评估的真实标签。所提出的网络架构融合了卷积、残差与注意力机制，以捕捉输入数据的局部与全局结构模式。该设计支持端到端训练以学习数据集的紧凑表示，并能够直接推荐最合适的聚类算法，从而降低对手工构建元特征和传统聚类有效性指标（CVIs）的依赖。在合成数据与真实世界基准测试上的综合实验表明，我们的深度学习模型持续优于传统CVIs（如轮廓系数、Calinski-Harabasz指数、Davies-Bouldin指数和Dunn指数）以及最先进的AutoML聚类推荐方法（如ML2DAC、AutoCluster和AutoML4Clust）。值得注意的是，该模型在合成数据上相比Calinski-Harabasz指数实现了0.497的ARI提升，在真实世界数据上相比性能最优的AutoML方法获得了15.3%的ARI增益。