Ensemble clustering: A practical tutorial
Caroline X. Gao, Shengqi Wang, Ye Zhu, Myriam Ziou, Shu Mei Teo, Catherine L. Smith, Derek Chiu, Aline Talhouk, Mengmeng Wang, Wenhua Yu, Sue M. Cotton, Dominic Dwyer · 2026
Read Ensemble clustering: A practical tutorial by Caroline X. Gao, Shengqi Wang, Ye Zhu, Myriam Ziou, Shu Mei Teo, Catherine L. Smith, Derek Chiu, Aline Talhouk, Mengmeng Wang, Wenhua Yu, Sue M. Cotton, Dominic Dwyer (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13541929.txt.
Description
Caroline Gao and co-authors offer a hands-on tutorial on ensemble clustering, in a methods paper from 2026. Clustering sorts mixed data into subgroups, a tool used widely in psychology and psychiatry. Single clustering runs can be unstable, so their results may not repeat or travel well to new data.
Ensemble clustering merges the answers from many runs, which may use varied algorithms, settings, or data samples, into one consensus. The tutorial sets out the core ideas and the main ways to build the parts and merge them. It walks readers through R libraries with worked examples and tuning advice. Tests on simulated and real-style data show the merged results hold up better than single runs.
Catalog record
| CREATOR | Caroline X. Gao, Shengqi Wang, Ye Zhu, Myriam Ziou, Shu Mei Teo, Catherine L. Smith, Derek Chiu, Aline Talhouk, Mengmeng Wang, Wenhua Yu, Sue M. Cotton, Dominic Dwyer |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 74 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |