Three-Way Clustering: An Advanced Soft Clustering Approach
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
Clustering is a machine learning technique that assigns unlabelled data points into different groups based on similarity of data. However, in many cases, we are unable to confidently assign data points to particular clusters. Soft clustering introduces a probability of the data points belonging to different clusters. Three-way clustering is a recent development of soft clustering based on three-way decisions. In particular, each data point is assigned a value to represent if it is inside, outside, or partially inside a cluster. There are two types of three-way clustering techniques, namely, evaluation-based approaches and operation-based approaches. The evaluation-based approaches rely on a membership function to calculate the degree of a data point belonging to a cluster. The operator-based approaches use a pair of operators to construct a three-way cluster from a hard two-way cluster. We will introduce, review, and analyse various three-way clustering techniques in this paper. In addition, the history of three-way clustering and the future development of three-way clustering will also be discussed.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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 it