Ensemble method for cluster number determination and algorithm selection in unsupervised learning [version 1; peer review: 2 approved with reservations, 1 not approved]

• Published in: F1000 research Vol. 11; p. 573
• Main Author: Zambelli, Antoine
• Format: Journal Article
• Language: English
• Published: 2022
• Subjects: K-means; Gaussian Mixture; Number of Clusters; Ensemble; Consensus Clustering; Hierarchical Clustering; Spectral Clustering; Unsupervised Learning; Clustering
• DOI: 10.12688/f1000research.121486.1
• ISSN: 2046-1402

Unsupervised learning, and more specifically clustering, suffers from the need for expertise in the field to be of use. Researchers must make careful and informed decisions on which algorithm to use with which set of hyperparameters for a given dataset. Additionally, researchers may need to determine the number of clusters in the dataset, which is unfortunately itself an input to most clustering algorithms; all of this before embarking on their actual subject matter work. After quantifying the impact of algorithm and hyperparameter selection, we propose an ensemble clustering framework which can be leveraged with minimal input. It can be used to determine both the number of clusters in the dataset and a suitable choice of algorithm to use for a given dataset. A code library is included in the Conclusions for ease of integration.