  The widespread adoption of machine learning (ML) techniques and the extensive
expertise required to apply them have led to increased interest in automated ML
solutions that reduce the need for human intervention. One of the main
challenges in applying ML to previously unseen problems is algorithm selection
- the identification of high-performing algorithm(s) for a given dataset, task,
and evaluation measure. This study addresses the algorithm selection challenge
for data clustering, a fundamental task in data mining that is aimed at
grouping similar objects. We present MARCO-GE, a novel meta-learning approach
for the automated recommendation of clustering algorithms. MARCO-GE first
transforms datasets into graphs and then utilizes a graph convolutional neural
network technique to extract their latent representation. Using the embedding
representations obtained, MARCO-GE trains a ranking meta-model capable of
accurately recommending top-performing algorithms for a new dataset and
clustering evaluation measure. Extensive evaluation on 210 datasets, 13
clustering algorithms, and 10 clustering measures demonstrates the
effectiveness of our approach and its superiority in terms of predictive and
generalization performance over state-of-the-art clustering meta-learning
approaches.
