A novel mixed values k-prototypes algorithm with application to health care databases mining
Why this work is in the frame
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Bibliographic record
Abstract
The current availability of large datasets composed of heterogeneous objects stresses the importance of large-scale clustering of mixed complex items. Several algorithms have been developed for mixed datasets composed of numerical and categorical variables, a well-known algorithm being the k-prototypes. This algorithm is efficient for clustering large datasets given its linear complexity. However, many fields are handling more complex data, for example variable-size sets of categorical values mixed with numerical and categorical values, which cannot be processed as is by the k-prototypes algorithm. We are proposing a variation of the k-prototypes clustering algorithm that can handle these complex entities, by using a bag-of-words representation for the multivalued categorical variables. We evaluate our approach on a real-world application to the clustering of administrative health care databases in Quebec, with results illustrating the good performances of our method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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