LVC: Local Variance-based Clustering
Bibliographic record
Abstract
Clustering has raised as an important problem in many different domains like biology, computer vision, text analysis and robotics. Thus, many different clustering techniques were developed to address this essential problem and propose astonishing solutions to conquer it. However, traditional clustering techniques suffer either from their limitations to detect specific shapes like K-means and PAM or from their limitations to detect clusters with specific densities as in DBSCAN and SNN. Moreover, exploiting the data relations and similarities has been proven to provide better insights to enhance the clustering quality as shown in spectral clustering and affinity propagation. Our observations have shown that using variance of similarities between each data point and its neighbors can well distinguish between within-cluster points, points connecting two clusters and outlier points. Therefore, we have utilized this variance measure to calculate each data point density and developed a Local Variance-based Clustering (LVC) technique that employs this measure to cluster the data. Experimental results show that LVC outperforms spectral clustering and affinity propagation in clustering quality using control charts, ecoli and images datasets, while maintaining a good running time. In addition, results show that LVC can detect topics from Twitter with higher topic recall by 15% and higher term precision by 3% over DBSCAN.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".