Multi-view neutrosophic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg" display="inline" id="d1e2958"><mml:mi>c</mml:mi></mml:math>-means clustering algorithms
Bibliographic record
Abstract
Multi-view clustering has become increasingly pervasive and prominent as multiple sources often provide different representations of information. However, existing multi-view clustering algorithms still encounter challenges since most multi-view data do not exhibit clear cluster boundaries, meaning cluster boundaries may locally overlap. Consequently, effectively characterizing and unveiling the imprecise and uncertain cluster structures in multi-view clustering remains an unresolved issue. Inspired by the robust capabilities of neutrosophic clustering in modeling imprecise and uncertain information, this paper introduces two novel multi-view neutrosophic c -means clustering algorithms , which can be regarded as derivatives of NCM in multi-view scenarios. The proposed algorithms are designed to represent the imprecision and uncertainty in cluster assignment of multi-view data while also autonomously discerning the importance of each view to boost clustering performance. We craft two objective functions and develop the corresponding optimization strategies to derive the neutrosophic partition matrix , view weight vector , and cluster centers matrix. Through extensive testing on both synthetic and real-world datasets, we demonstrate the practicality and effectiveness of our proposed algorithms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".