CLAMS: A System for Zero-Shot Model Selection for Clustering
This work addresses the need for efficient algorithm selection in clustering applications, extending AutoML beyond supervised learning, though it appears incremental as it builds on existing similarity methods.
The paper tackles the problem of automated model selection for clustering tasks by proposing an AutoML system that uses optimal transport-based dataset similarity, and it demonstrates that this system outperforms multiple clustering baselines.
We propose an AutoML system that enables model selection on clustering problems by leveraging optimal transport-based dataset similarity. Our objective is to establish a comprehensive AutoML pipeline for clustering problems and provide recommendations for selecting the most suitable algorithms, thus opening up a new area of AutoML beyond the traditional supervised learning settings. We compare our results against multiple clustering baselines and find that it outperforms all of them, hence demonstrating the utility of similarity-based automated model selection for solving clustering applications.