Likelihood Based Fuzzy Clustering for Data Sets of Mixed Features
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
A noble clustering algorithm is presented for data sets of mixed features: numerical, ordinal and nominal. The algorithm uses the concept of fuzzy clustering to reduce negative effect from noises, and uses the iterative partitional algorithm founded on an optimization function to reduce the time complexity. The optimization function uses the likelihood for each individual feature as the optimization criterion of the similarity or likeliness between patterns and clusters, not like the fuzzy c-means clustering algorithm based on distance or the EM clustering algorithm. Hence the algorithm can quickly find fuzzy clusters having different distributions in the each feature level. The simulations show the algorithm to be quite efficient
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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