A Novel Clustering Framework for Stream Data Un nouveau cadre de classifications pour les données de flux
Bibliographic record
Abstract
There is a growing tendency for developing real-time clustering of continuous stream data. In this regard, a few attempts have been made to improve the off-line phase of stream clustering methods, whereas these methods almost use a simple distance function in their online phase. In practice, clusters have complex shapes, and therefore, measuring the distance of incoming samples to the mean of asymmetric microclusters might mislead incoming samples to irrelevant microclusters. In this paper, a novel framework is proposed, which can enhance the online phase of all stream clustering methods. In this manner, for each microcluster for which its population exceeds a threshold, a classifier is exclusively trained to capture its boundary and statistical properties. Thus, incoming samples are assigned to the microclusters according to the classifiers⣙ scores. Here, the incremental NaÃve Bayes classifier is chosen, due to its fast learning property. DenStream and CluStream as the state-of-the-art methods were chosen and their performance was assessed over nine synthetic and real data sets, with and without applying the proposed framework. The comparative results in terms of purity, general recall, general precision, concept change traceability, computational complexity, and robustness against noise over the data sets imply the superiority of the modified methods to their original versions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".