1. FastEnsemble: A new scalable ensemble clustering method
- Author
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Tabatabaee, Yasamin, Wedell, Eleanor, Park, Minhyuk, and Warnow, Tandy
- Subjects
Computer Science - Social and Information Networks - Abstract
Many community detection algorithms are stochastic in nature, and their output can vary based on different input parameters and random seeds. Consensus clustering methods, such as FastConsensus and ECG, combine clusterings from multiple runs of the same clustering algorithm, in order to improve stability and accuracy. In this study we present a new consensus clustering method, FastEnsemble, and show that it provides advantages over both FastConsensus and ECG. Furthermore, FastEnsemble is designed for use with any clustering method, and we show results using \ourmethod with Leiden optimizing modularity or the Constant Potts model. FastEnsemble is available in Github at https://github.com/ytabatabaee/fast-ensemble, Comment: 12 pages, 5 figures, submitted to a conference
- Published
- 2024