An Approximate Maximin-Directed Random Sampling for Clustering Applications
Bibliographic record
Abstract
The Maximin-Directed Random Sampling (MMDRS) algorithm, a cornerstone of numerous visual assessment techniques and scalable single linkage clustering, is recognized for its unique three-part structure: (i) Maximin (MM) sampling for prototype identification;(ii) nearest prototype partition construction via maximin samples; and (iii) directed random sampling from partition subsets.Despite its diverse applications, the computational complexity of MMDRS presents significant challenges.In response to this issue, an approximate form of the MMDRS algorithm (AMMDRS) is proposed in this study, aiming to alleviate time complexity.Through experimental investigation, comparisons are drawn between the directed random sampling methods, assessing whether significant differences exist in the samples produced and evaluating the superiority of either method over simple random sampling.The results of this empirical study demonstrate that AMMDRS outperforms MMDRS in terms of speed across all datasets, without any compromise on sampling accuracy.This finding underscores the critical importance of such a method in big data applications, where the feasibility of processing the entire dataset is often limited.The study's revelations emphasize that undirected random sampling achieves more authentic representations of parent distributions than MM samples alone, thereby maximizing the diversity and representativeness of selected points within the feature space.Overall, this study introduces a promising avenue for enhancing the efficiency of MMDRS, opening the door to its broader application in data-intensive domains.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".