Incremental Cluster Validity Index for Predicting Early Signs of Change in Data Streams
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose an incremental version of the SD cluster validity index for streaming data monitoring and predicting early signs of change. The proposed incremental SD (iSD) is used to monitor the data stream along with the MU Streaming Clustering (MUSC) algorithm. We investigate the use of iSD for detecting early signs of changes in multiple data streams arriving at the same time. Synthetic and real-life datasets are used in the analysis to demonstrate the effectiveness of the proposed index in detecting early signs of changes in the data stream. Valuable information about the streaming data can be directly captured from the index values such as the appearance of new patterns, and cluster size based on the analysis of outliers. The performance of iSD is compared with the incremental Davies-Boudin index (iDB). iSD has larger values which makes it more robust in monitoring large data streams compared to iDB which tends to flatten over time and approaches zero.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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