Implications of Different Encodings of Binned Data when Clustering
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
Abstract When using clustering to uncover patterns in a dataset, a data analyst must make several decisions. In some cases, one of those decisions is how to handle binned data (e.g., age or income bands), which is a common data type collected in surveys. When clustering, it is possible to encode this variable as a nominal, ordinal, or interval-scaled variable (e.g., using the bin’s midpoint), and it is not clear which of these encodings, if any, should be preferred over others. We examined the impacts of these encodings on clustering results obtained from four clustering algorithms: partitioning around medoids (PAM) with Gower’s distance, K-prototypes, a latent class model, and KAMILA, on several simulated datasets and three household finance survey datasets from North America. We found that the optimal encoding varies depending on the clustering algorithm. We recommend the nominal encoding for latent class models, the ordinal encoding for K-prototypes and KAMILA (although the results were less definitive for these two), and the midpoint encoding for PAM.
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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 | 0.001 | 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