Clustering Algorithms with Balanced Weights for Geographic Data Processing
Bibliographic record
Abstract
With the rapid growth of geographic data, generated by various sensors and end equipment, new opportunities for research and practical applications can be found in various applications. However, effective utilization of this data often requires the division of geospatial space into smaller, manageable regions. An important challenge is to ensure that these regions with closed data points are balanced in terms of data size distribution (e.g., population density, resource allocation, etc.), creating a double optimization problem. The contributions of this paper are twofold. First, we propose a balance-driven partitioning algorithm, which is a coordinate-descent based algorithm using a dynamic programming technique. Second, we present a clustering-centric algorithm that improves the classic k-means algorithm with an imbalance-penalized function to allow the geographic data to be clustered together not only in terms of geographic location, but also in terms of the per-cluster total sizes in balance. Finally, to evaluate the efficiency of the proposed algorithms, we conducted experiments based on a trace geographic dataset and compared the results with those of the existing clustering algorithms. Our results demonstrate that the proposed algorithms can not only achieve the competitive clustering effects but also exhibit better performance in terms of data-size balance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".