Evolutionary clustering with arbitrary subspaces
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
Subspace clustering algorithms in their most general form attempt to describe data with clusters that are not constrained to index a common set of attributes. Previous evolutionary approaches to this problem have assumed a weaker model in which clusters are built in a common subset. Moreover, a filter method is generally assumed in which a classical clustering algorithm is employed in the inner loop. Needless to say, this presents a considerable computational overhead. In this work we recognize the utility of assuming a `bottom-up' approach to subspace clustering. Specifically, we apply a classical clustering algorithm to each attribute to establish 1-d clusters that are then indexed by a MOGA to design a population of subspace clusters. The ensuing search is entirely in terms of a combinatorial optimization problem, thus computationally very efficient. A final single objective GA is then applied to search the set of subspace clusters identified under the MOGA for the most suitable combination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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