Empirical Study of Performance of Classification and Clustering Algorithms on Binary Data with Real-World Applications
Bibliographic record
Abstract
This thesis compares statistical algorithms paired with dissimilarity measures for their ability to identify clusters in benchmark binary datasets.The techniques examined are visualization, classification, and clustering.To visually explore for clusters, we used parallel coordinates plots and heatmaps.The classification algorithms used were neural networks and classification trees.Clustering algorithms used were: partitioning around centroids, partitioning around medoids, hierarchical agglomerative clustering, and hierarchical divisive clustering.5.6 Classification tree -complete and pruned on the raw zoo data . . . .5.7 Zoo optimization graph . . . . . . . . . . . . . . . . . . . . . . . . . .5.8 ASW versus the number of clusters for the partitioning algorithms (zoo) 5.9 ASW versus the number of clusters for the hierarchical algorithms (zoo) 5.10 ASW versus the number of clusters for the remaining hierarchical algorithms (zoo) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .C.1 Residual analysis using "best" k response variable . . . . . . . . . . .C.2 Residual analysis using ASW response variable . . . . . . . . . . . . .
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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 | 0.020 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".