FastEnsemble: A new scalable ensemble clustering method

Yasamin Tabatabaee, Eleanor Wedell, Minhyuk Park, Tandy Warnow
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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
FastEnsemble:一种新的可扩展集合聚类方法
许多群落检测算法都具有随机性,其输出结果会根据不同的输入参数和随机种子而变化。共识聚类方法,如 FastConsensus 和 ECG,将同一聚类算法多次运行的聚类结果结合起来,以提高稳定性和准确性。在这项研究中,我们提出了一种新的共识聚类方法--FastEnsemble,并证明它比 FastConsensus 和 ECG 都更有优势。此外,FastEnsemble还可以与任何聚类方法一起使用,我们展示了使用莱顿优化模块化或康斯坦茨-波茨模型(Constant Potts model)的结果。FastEnsemble可在Github上下载:https://github.com/ytabatabaee/fast-ensemble。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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