Hybrid Metaheuristic Algorithm for Clustering
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".