Tensor-Based Multiple Clustering Approaches for Cyber-Physical-Social Applications
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
In multiple analysis tasks and personalized services, tremendous challenges in Cyber-Physical-Social Systems (CPSS) are clustering large-scale multi-source data and generating multiple distinct clusterings dependent on different applications. To address these challenges, this paper first presents two simple multiple clustering methods which can produce different clustering results according to arbitrarily selected combinations of features, one is similarity matrices-based multiple clusterings which computes the weighted average of similarity matrices for selected feature spaces, another is Euclidean distance-based multiple clusterings which fuses different feature spaces using selective weighted Euclidean distance. Furthermore, a tensor decomposition-based multiple clusterings is presented for efficiently clustering high-dimensional data, and a multi-relational attribute ranking method is further proposed to improve the clustering performance. This paper illustrates and evaluates the proposed methods on a design example and a real world data set. Experimental results show that the proposed methods can effectively cluster big data to provide enhanced knowledge extractions and services in CPSS.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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