Ensemble Method for Cluster Number Determination and Algorithm Selection in Unsupervised Learning

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
Main Author Zambelli, Antoine
Format Journal Article
LanguageEnglish
Published 22.12.2021
Subjects
Computer Science - Learning
Quantitative Biology - Quantitative Methods
Statistics - Methodology
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Summary: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 Conclusion for ease of integration.
DOI:10.48550/arxiv.2112.13680