Incremental document clustering using cluster similarity histograms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clustering of large collections of text documents is a key process in providing a higher level of knowledge about the underlying inherent classification of the documents. Web documents, in particular, are of great interest since managing, accessing, searching, and browsing large repositories of Web content requires efficient organization. Incremental clustering algorithms are always preferred to traditional clustering techniques, since they can be applied in a dynamic environment such as the Web. An incremental document clustering algorithm is introduced, which relies only on pair-wise document similarity information. Clusters are represented using a cluster similarity histogram, a concise statistical representation of the distribution of similarities within each cluster, which provides a measure of cohesiveness. The measure guides the incremental clustering process. Complexity analysis and experimental results are discussed and show that the algorithm requires less computational time than standard methods while achieving a comparable or better clustering quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it