Hybrid Metaheuristic Algorithm for 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
Clustering involves grouping a collection of data objects into meaningful or useful categories such that objects within the same category are similar to one another while objects in different categories are dissimilar. Clustering is a challenging problem with diverse practical applications that span multiple research domains. A review of existing literature shows that there are many diverse clustering algorithms for different problem domains. Also, many popular optimization heuristics and metaheuristics have been adapted to create clustering algorithms, but these algorithms typically inherit the limitations of the underlying heuristics or metaheuristics. An evolving trend in metaheuristic algorithm design is to combine concepts and/or components from multiple algorithms to tackle difficult optimization problems such as clustering. In this research, we explore the possibility of harnessing the strengths of multiple metaheuristic algorithms to tackle the clustering problem. We propose a hybrid metaheuristic algorithm for clustering that combinesant brood sorting (a nature-inspired clustering technique) with tabu search (a metaheuristic that uses search history and dynamic neighborhood strategies to uncover global optimal solution). This is a new hybrid metaheuristic approach to clustering with emphasis on flexibility and less specificity.
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 | 0.000 |
| 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