A Comprehensive Study of Ensemble Models to Improve the Performance of Cluster Algorithms
Bibliographic record
Abstract
The study analyzed the individual performance of partition cluster algorithms and selected Kmeans, Kmeans 9+ , Kmedoid, and Fuzzy Cmeans algorithms as base algorithms for the ensemble.The cluster performance is assessed using UCI data sets as well as other common public data sets.The quality of cluster results depends on the base cluster algorithm used.The efficiency of base algorithms is added based on the ensemble models.We developed two ensemble models: a simple hard voting ensemble and a soft boosting ensemble based on the bagging and boosting ensemble technique.Ensemble of different cluster algorithms can generate the most accurate clusters.Both models show better cluster results than their base cluster algorithms for the small and big data sets.When using most data sets, the Soft Boosting Ensemble model achieves 100% cluster accuracy.The cluster evaluating functions are the benchmark for assessing the quality of the cluster.All the cluster evaluating indices show better performance for developed ensemble models.Internal cluster-evaluating indices as well as external cluster-evaluating indices are used to compare the cluster quality of the individual cluster algorithm and generated ensemble cluster models.The work establishes that the developed ensemble methods improved the quality of the generated clusters.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".