A Method for Fuzzy Clustering with Ordinal Attributes Replaced by Fuzzy Set Parameters
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Bibliographic record
Abstract
Pattern vectors to be clustered may have attributes of various types including ordinal. The latter type of attribute with values such as "poor", "very poor", "good", and "very good" are neither entirely numerical nor entirely qualitative. This leads to difficulties in clustering since it is meaningless to take differences of values of these ordinal attributes as is required for finding distance between pattern vectors. Representing ordinal values by numbers and then finding differences are incorrect. Rather the ordinal values themselves may considered as linguistic values of linguistic variables corresponding to fuzzy sets. This paper discusses a method of fuzzy c-means clustering that uses the moments and areas of fuzzy sets to represent the value of ordinal attributes and also the continuous values of the interval scaled attributes
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 it