Soon: self organising oscillator networks for use in clustering problems
Bibliographic record
Abstract
The self-organising oscillator network (SOON) is a comparatively new clustering algorithm [H.F.M.B.H. Rhouma, February 2001], that has received relatively little attention so far. The SOON is distance based, meaning that clustering behaviour is different in a number of ways that can be beneficial. This paper examines the effect of adjusting the control parameters of the SOON with two widely different datasets which represent two different types of real-world data; the first is a communications signal dataset representing one modulation scheme under a variety of noise conditions. The second is a biological dataset taken from microarray experiments on the cell-cycle of yeast. The modulation scheme data is relatively easy to cluster at high SNR, however at lower SNR, the clustering problem becomes much more difficult as the separation between the cluster reduces. The paper demonstrates that the SOON is a viable tool to analyse these problems, and can add many useful insights to the data, that may not always be available using other clustering methods
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".