Discovering Density-Based Clustering Structures Using Neighborhood Distance Entropy Consistency
Bibliographic record
Abstract
Traditional clustering algorithms model the clustering problem as an optimization task, in which the objective is defined based on minimizing specific metrics. These algorithms are limited to find clusters with convex polytopes. In contrast, density-based clustering algorithms aim at overcoming this limitation and try to partition data objects into meaningful groups that have relatively high density separated by low-density regions. This work describes and evaluates a new density-based clustering algorithm, called neighborhood distance entropy consistency (NDEC), which is able to not only detect clusters of arbitrary size, shape, and density, but also identify outliers. To this end, both local and global densities are considered simultaneously to accurately discover the intrinsic clustering structure. In addition, the consistency of neighborhood distance entropy is used as an important criterion to merge potential subclusters. Experiments on synthetic and real benchmark clustering data sets have demonstrated the efficiency and effectiveness of the NDEC method. Comparisons with k-means, DBSCAN, OPTICS, and density peaks clustering algorithms further show that NDEC can successfully discover natural clusters. Additionally, the utility of NDEC is demonstrated with its application on two real-world problems including brain white matter tracts segmentation using diffusion tensor imaging and characterizing motor unit potential trains extracted from electromyographic signals.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".