Symbiotic evolutionary subspace 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
New emerging high-dimensional data sets have made traditional clustering algorithms increasingly inefficient. More sophisticated approaches are required to cope with the increasing dimensionality and cardinality of such data sets. Feature selection methods are proposed as a solution to deal with this problem, however they fail for data sets where the attribute support for different clusters is not the same. For this category of data sets subspace clustering algorithms have been introduced over the past decade. We approach this problem from the perspective of Genetic Algorithms by adopting a hierarchical data structure deployed in three stages. 1) a traditional clustering algorithm is applied independently to each attribute of the data set, thus defining a grid of potential 1-d cluster centroids. 2) representing multi-dimensional cluster centroids by indexing 1-d cluster centroids. 3) converting the problem of finding the best combination of cluster centroids into that of discrete optimization and applying a multi-objective evolutionary algorithm, which uses group fitness evaluation to give a fitness to a group of clusters, as defined by process 2. Synthetic data sets with different characteristics are generated as the ground truth to evaluate the resulting algorithm for Evolutionary Subspace Clustering (ESC) as well as benchmark against alternative subspace and full-space clustering algorithms. ESC returns competitive accuracy and while typically utilizing less attributes and scaling as attribute count increases.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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