An Edge-Cloud-Aided High-Order Possibilistic c-Means Algorithm for Big Data Clustering
Bibliographic record
Abstract
In this article, a high-order possibilistic c-means algorithm (HOPCM) based on the double-layer deep computation model (DCM) is proposed for big data clustering. Specifically, an asymmetric tensor autoencoder is presented to efficiently train the double-layer DCM for big data feature learning. Furthermore, an edge-cloud computing system is developed to improve the clustering efficiency. In the edge-cloud system, the computation-intensive tasks including the parameters' training and clustering are offloaded to the cloud while the task of feature learning is performed at the edge of network. Finally, we conduct extensive experiments to evaluate the performance of the presented algorithm by comparing it with other two representative big data clustering algorithms, i.e., the standard HOPCM and the HOPCM based on deep learning. Results demonstrate that the presented algorithm achieves higher accuracy than the two compared algorithms and furthermore the clustering efficiency are significantly improved by the developed edge-cloud computing system.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".