Distributed K-means Clustering Using Topological Relationships
Why this work is in the frame
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Bibliographic record
Abstract
Cluster analysis has been widely studied due to its importance and several methods have been developed for this purpose.However, these methods are designed to process on centralized data.In this paper, we present an asynchronous approach based on topological relationship.The proposed approach unfolds on three steps: First, each site searches for clusters (models) of its local data.Secondly, a central site proceeds to the analysis and search for the partition of the whole (the global model).Finally, we proceed with the search for the right number of groups of the global model.We note that each local data has their own number of clusters and it can be different from the number of clusters in the entire data.The experiments have clearly demonstrated the effectiveness of the proposed approach to find the partition closest to that obtained on the data set from its subsets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 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