Multi-View Relational Evidential C-Medoid Clustering with Adaptive Weighted
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
Relational data, where objects are defined by similarities or dissimilarities, is omnipresent and essential in real clustering applications. In this context, relational clustering, which aims to identify groups of similar objects based on their mutual relationships, has become a necessity. However, most existing relational clustering methods cannot effectively handle multi-view data sets while representing uncertainty and imprecision when faced with objects in overlapping clusters. To address this gap, we introduce a new relational clustering method, called Multi-View Evidential C-Medoid clustering with adaptive weightings (MECMdd). Our approach is based on the theory of belief functions to characterize the partial knowledge in cluster assignment. It integrates view weight assignments, estimated locally for each cluster and globally in a collaborative learning framework. We have evaluated our proposition via several experiments using different real-world datasets, compared to other related and state-of-the-art methods, in terms of their advantages and overall effectiveness.
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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 | 0.000 | 0.000 |
| 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