Go to Computo homepageSpectral Bridges
Abstract: In this paper, Spectral Bridges, a novel clustering algorithm, is introduced. This algorithm builds upon the traditional k-means and spectral clustering frameworks by subdividing data into small Voronoï regions, which are subsequently merged according to a connectivity measure. Drawing inspiration from Support Vector Machine's margin concept, a non-parametric clustering approach is proposed, building an affinity margin between each pair of Voronoï regions. This approach is characterized by minimal hyperparameters and delineation of intricate, non-convex cluster structures.
The numerical experiments underscore Spectral Bridges as a fast, robust, and versatile tool for sophisticated clustering tasks spanning diverse domains. Its efficacy extends to large-scale scenarios encompassing both real-world and synthetic datasets.
The Spectral Bridge algorithm is implemented both in Python (<https://pypi.org/project/spectral-bridges>) and R <https://github.com/cambroise/spectral-bridges-Rpackage>).
Repository Url: https://github.com/cambroise/spectral-bridges-computo/
Assigned Action Editor: ~Julien_Chiquet1
Submission Number: 12
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