Ensemble clustering: A practical tutorial
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cluster analysis is an explorative analytical method, serving as a critical tool in psychology, psychiatry and related fields to map heterogeneous data into meaningful subgroups. Despite their extensive historical use, traditional clustering techniques suffer from a lack of stability, robustness, and generalisability. These issues stem from the inherent difficulties of the clustering optimization problem as well as the stochastic nature of algorithm optimizers. To address these challenges, we demonstrate the use of methods utilising ensemble learning techniques to combine clustering results from different algorithms, model specifications, and/or sampled sub-datasets to form a single, more reliable consensus of clustering solutions. We detail ensemble clustering principles, variations in base clustering generation models, and consensus methods. Detailed introductions in existing R libraries and practical examples using R code are provided to guide users in both implementing and optimising ensemble clustering models. We then include simulation studies of real-world data to demonstrate the substantial benefit of ensemble clustering compared with single-run clustering models. The resources presented here will enable researchers to apply advanced clustering techniques to decompose heterogeneous and complex psychological data into stable subgroups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it