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

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 determin...

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Bibliographic Details

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
ISSN:	2046-1402, 2046-1402
Online Access:	Get full text
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