Tensor Train-Based Multiple Clusterings for Big Data in Cyber-Physical-Social Systems and Its Efficient Implementations
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
Multiple clusterings are conducive to discovering different data patterns hidden in data from different perspectives, so it has tremendous value in applications like community detection, resource recommendation, and gene expression, etc. To solve the problem that the existing multiple clustering approaches are mainly oriented to low-dimensional single-domain data and are not suitable for Big Data in Cyber-Physical-Social Systems (CPSS), a tensor-based multiple clustering (TMC) was proposed. However, as the scale of data continues to increase, data storage, computing load, and memory overhead will increase exponentially, leading to dimensional disasters and greatly affecting the efficiency of TMC. Therefore, a tensor train-based multiple clustering (TTMC) and its parallel computing method are studied in this paper. First, a tensor train (TT)-based multiple clustering parallel analytic and service framework is present. Then, a TT-based multi-linear attribute combination weight learning algorithm, a selective weighted tensor train distance, and the TTMC algorithm are put forward to improve the accuracy and efficiency of TMC. Furthermore, an efficient distributed parallel computing strategy of TTMC is designed by using TT core parallelism. Experimental results demonstrate that TTMC and its parallelization can significantly improve computation efficiency and clustering accuracy while reducing the running memory compared to the original TMC algorithm.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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