Incremental and effective data summarization for dynamic hierarchical clustering
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
Mining informative patterns from very large, dynamically changing databases poses numerous interesting challenges. Data summarizations (e.g., data bubbles) have been proposed to compress very large static databases into representative points suitable for subsequent effective hierarchical cluster analysis. In many real world applications, however, the databases dynamically change due to frequent insertions and deletions, possibly changing the data distribution and clustering structure over time. Completely reapplying both the data summarization and the clustering algorithm to detect the changes in the clustering structure and update the uncovered data patterns following such deletions and insertions is prohibitively expensive for large fast changing databases. In this paper, we propose a new scheme to maintain data bubbles incrementally. By using incremental data bubbles, a high-quality hierarchical clustering is quickly available at any point in time. In our scheme, a quality measure for incremental data bubbles is used to identify data bubbles that do not compress well their underlying data points after certain insertions and deletions. Only these data bubbles are re-built using efficient split and merge operations. An extensive experimental evaluation shows that the incremental data bubbles provide significantly faster data summarization than completely re-building the data bubbles after a certain number of insertions and deletions, and are effective in preserving (and in some cases even improving) the quality of the data summarization.
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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 | 0.001 |
| 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