用深度学习自动推荐最适合数据的聚类算法，省去人工试错。
ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
- 直接从原始表格数据学高阶特征，跳过手动特征工程。
- 合成数据上比传统指标高0.497 ARI，真实数据上超领先AutoML方案44.16% ARI。
- 适合想快速选聚类算法的研究者或工程师，尤其处理复杂数据时。
为给定数据集选择有效的聚类算法仍是无监督学习中的核心问题。本文提出ClustRecNet，一种端到端深度学习框架，通过直接学习原始表格数据的高阶表示来推荐合适的聚类算法。为支持鲁棒元学习，我们构建了包含34,000个合成数据集的综合性库，涵盖多种聚类场景，运行10种主流聚类算法，并使用调整兰德指数（ARI）建立真实标签。ClustRecNet采用卷积块、两个残差块和注意力块，捕捉局部与全局结构模式，有效克服人工特征工程的知识瓶颈。在合成与真实世界基准上的大量评估表明，其性能持续优于传统内部聚类有效性指标（如Silhouette、Calinski-Harabasz、Davies-Bouldin、Dunn）以及最先进的AutoML方法（如ML2DAC、AutoCluster、AutoML4Clust）。例如，在合成数据上平均比Calinski-Harabasz指标高出0.497 ARI；在真实数据集上平均比领先AutoML方法（ML2DAC）提升44.16% ARI。代码与数据已公开于：https://github.com/mrbakhtyari/ClustRecNet
原文摘要 · Abstract (English)
Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue. We introduce ClustRecNet, a novel end-to-end deep learning framework that recommends suitable clustering algorithm(s) by directly learning high-order representations of raw tabular data. To facilitate robust meta-learning, we first construct a comprehensive repository of 34,000 synthetic datasets encompassing a large variety of clustering scenarios, run 10 popular clustering algorithms, and use Adjusted Rand Index (ARI) to establish ground-truth labels. ClustRecNet's architecture incorporates a convolution block, two residual blocks, and an attention block to capture local and global structural patterns, effectively bypassing the knowledge bottleneck associated with manual feature engineering. Extensive evaluation on both synthetic and real-world benchmarks demonstrates that ClustRecNet consistently outperforms traditional internal cluster validity indices such as Silhouette, Calinski-Harabasz, Davies-Bouldin, and Dunn as well as state-of-the-art Automated Machine Learning (AutoML) approaches such as ML2DAC, AutoCluster, and AutoML4Clust. For example, our framework achieves an average 0.497 ARI gain over the Calinski-Harabasz cluster validity index on synthetic data and an average 44.16% ARI improvement over the leading AutoML approach (ML2DAC) on real-world benchmarks. Code and data are available at: https://github.com/mrbakhtyari/ClustRecNet
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