CLAMS: A System for Zero-Shot Model Selection for Clustering

Autor:	Singh, Prabhant, Gijsbers, Pieter, Yildirim, Murat Onur, Gok, Elif Ceren, Vanschoren, Joaquin
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.11286 Zobrazit plný text záznamu View this record from Arxiv